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Kwok WH, Zhang Y, Wang G. Artificial intelligence in perinatal mental health research: A scoping review. Comput Biol Med 2024; 177:108685. [PMID: 38838557 DOI: 10.1016/j.compbiomed.2024.108685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 04/28/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.
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Affiliation(s)
- Wai Hang Kwok
- School of Nursing and Midwifery, Edith Cowan University, WA, Australia
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, China
| | - Guanjin Wang
- School of Information Technology, Murdoch University, Murdoch, WA, Australia.
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2
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Tran VT, Gartlehner G, Yaacoub S, Boutron I, Schwingshackl L, Stadelmaier J, Sommer I, Alebouyeh F, Afach S, Meerpohl J, Ravaud P. Sensitivity and Specificity of Using GPT-3.5 Turbo Models for Title and Abstract Screening in Systematic Reviews and Meta-analyses. Ann Intern Med 2024. [PMID: 38768452 DOI: 10.7326/m23-3389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Systematic reviews are performed manually despite the exponential growth of scientific literature. OBJECTIVE To investigate the sensitivity and specificity of GPT-3.5 Turbo, from OpenAI, as a single reviewer, for title and abstract screening in systematic reviews. DESIGN Diagnostic test accuracy study. SETTING Unannotated bibliographic databases from 5 systematic reviews representing 22 665 citations. PARTICIPANTS None. MEASUREMENTS A generic prompt framework to instruct GPT to perform title and abstract screening was designed. The output of the model was compared with decisions from authors under 2 rules. The first rule balanced sensitivity and specificity, for example, to act as a second reviewer. The second rule optimized sensitivity, for example, to reduce the number of citations to be manually screened. RESULTS Under the balanced rule, sensitivities ranged from 81.1% to 96.5% and specificities ranged from 25.8% to 80.4%. Across all reviews, GPT identified 7 of 708 citations (1%) missed by humans that should have been included after full-text screening at the cost of 10 279 of 22 665 false-positive recommendations (45.3%) that would require reconciliation during the screening process. Under the sensitive rule, sensitivities ranged from 94.6% to 99.8% and specificities ranged from 2.2% to 46.6%. Limiting manual screening to citations not ruled out by GPT could reduce the number of citations to screen from 127 of 6334 (2%) to 1851 of 4077 (45.4%), at the cost of missing from 0 to 1 of 26 citations (3.8%) at the full-text level. LIMITATIONS Time needed to fine-tune prompt. Retrospective nature of the study, convenient sample of 5 systematic reviews, and GPT performance sensitive to prompt development and time. CONCLUSION The GPT-3.5 Turbo model may be used as a second reviewer for title and abstract screening, at the cost of additional work to reconcile added false positives. It also showed potential to reduce the number of citations before screening by humans, at the cost of missing some citations at the full-text level. PRIMARY FUNDING SOURCE None.
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Affiliation(s)
- Viet-Thi Tran
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Centre for Research in Epidemiology and Statistics (CRESS), Paris; and Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu, AP-HP, Paris, France (V.-T.T.)
| | - Gerald Gartlehner
- Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Krems, Austria; and Center for Public Health Methods, RTI International, Research Triangle Park, North Carolina (G.G.)
| | - Sally Yaacoub
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Centre for Research in Epidemiology and Statistics (CRESS), Paris, France (S.Y., F.A.)
| | - Isabelle Boutron
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Centre for Research in Epidemiology and Statistics (CRESS), Paris, France; and Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu, AP-HP, Paris, France (I.B.)
| | - Lukas Schwingshackl
- Institute for Evidence in Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (L.S., J.S., J.M.)
| | - Julia Stadelmaier
- Institute for Evidence in Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (L.S., J.S., J.M.)
| | - Isolde Sommer
- Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Krems, Austria (I.S.)
| | - Farzaneh Alebouyeh
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Centre for Research in Epidemiology and Statistics (CRESS), Paris, France (S.Y., F.A.)
| | - Sivem Afach
- Epidemiology in Dermatology and Evaluation of Therapeutics (EpiDermE)-EA 7379, University Paris Est Créteil Val de Marne, Créteil, France (S.A.)
| | - Joerg Meerpohl
- Institute for Evidence in Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (L.S., J.S., J.M.)
| | - Philippe Ravaud
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Centre for Research in Epidemiology and Statistics (CRESS), Paris, France; Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu, AP-HP, Paris, France; and Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York (P.R.)
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3
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Huo X, Ong KH, Lau KW, Gole L, Young DM, Tan CL, Zhu X, Zhang C, Zhang Y, Li L, Han H, Lu H, Zhang J, Hou J, Zhao H, Gan H, Yin L, Wang X, Chen X, Lv H, Cao H, Yu X, Shi Y, Huang Z, Marini G, Xu J, Liu B, Chen B, Wang Q, Gui K, Shi W, Sun Y, Chen W, Cao D, Sanders SJ, Lee HK, Hue SSS, Yu W, Tan SY. A comprehensive AI model development framework for consistent Gleason grading. COMMUNICATIONS MEDICINE 2024; 4:84. [PMID: 38724730 PMCID: PMC11082180 DOI: 10.1038/s43856-024-00502-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 04/17/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. METHODS We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. RESULTS Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. CONCLUSIONS This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.
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Affiliation(s)
- Xinmi Huo
- Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore
| | - Kok Haur Ong
- Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore
| | - Kah Weng Lau
- Computational & Molecular Pathology Lab, Institute of Molecule and Cell Biology, A*STAR, Singapore, Singapore
- Department of Pathology, National University Hospital, National University Health System, Singapore, Singapore
| | - Laurent Gole
- Computational & Molecular Pathology Lab, Institute of Molecule and Cell Biology, A*STAR, Singapore, Singapore
| | - David M Young
- Computational & Molecular Pathology Lab, Institute of Molecule and Cell Biology, A*STAR, Singapore, Singapore
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, USA
| | - Char Loo Tan
- Department of Pathology, National University Hospital, National University Health System, Singapore, Singapore
| | - Xiaohui Zhu
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou, Guangdong Province, China
- Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou, Guangdong Province, China
| | - Chongchong Zhang
- Department of Pathology, The 910 Hospital of PLA, QuanZhou, Fujian Province, China
| | - Yonghui Zhang
- Department of Pathology, The 910 Hospital of PLA, QuanZhou, Fujian Province, China
| | - Longjie Li
- Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore
| | - Hao Han
- Computational & Molecular Pathology Lab, Institute of Molecule and Cell Biology, A*STAR, Singapore, Singapore
| | - Haoda Lu
- Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology (NUIST), Nanjing, Jiangsu Province, China
| | - Jing Zhang
- Department of Pathology, Shanghai Changzheng Hospital, Shanghai, China
| | - Jun Hou
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Huanfen Zhao
- Department of Pathology, Hebei General Hospital, Shijiazhuang, Hebei Province, China
| | - Hualei Gan
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Lijuan Yin
- Department of Pathology, Changhai Hospital of Shanghai, Shanghai, China
| | - Xingxing Wang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaoyue Chen
- Department of Pathology, Hebei General Hospital, Shijiazhuang, Hebei Province, China
| | - Hong Lv
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Haotian Cao
- Department of Pathology, Shanghai Changzheng Hospital, Shanghai, China
| | - Xiaozhen Yu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yabin Shi
- Department of Pathology, Hebei General Hospital, Shijiazhuang, Hebei Province, China
| | - Ziling Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Gabriel Marini
- Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore
| | - Jun Xu
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology (NUIST), Nanjing, Jiangsu Province, China
| | - Bingxian Liu
- Ningbo KonFoong Bioinformation Tech Co. Ltd, Ningbo, Zhejiang Province, China
| | - Bingxian Chen
- Ningbo KonFoong Bioinformation Tech Co. Ltd, Ningbo, Zhejiang Province, China
| | - Qiang Wang
- Ningbo KonFoong Bioinformation Tech Co. Ltd, Ningbo, Zhejiang Province, China
| | - Kun Gui
- Ningbo KonFoong Bioinformation Tech Co. Ltd, Ningbo, Zhejiang Province, China
| | - Wenzhao Shi
- Vishuo Biomedical Pte Ltd, Singapore, Singapore
| | - Yingying Sun
- Cancer Center, Department of Pathology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang Province, China
| | - Wanyuan Chen
- Cancer Center, Department of Pathology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang Province, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, Zhejiang Province, China
- Clinical Research Center for Cancer of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Dalong Cao
- Department of Urology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
- Shanghai Genitourinary Cancer Institute, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, USA
- Institute of Developmental and Regenerative Medicine, University of Oxford, Oxford, UK
| | - Hwee Kuan Lee
- Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore
| | - Susan Swee-Shan Hue
- Computational & Molecular Pathology Lab, Institute of Molecule and Cell Biology, A*STAR, Singapore, Singapore.
- Department of Pathology, National University Hospital, National University Health System, Singapore, Singapore.
| | - Weimiao Yu
- Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore.
- Computational & Molecular Pathology Lab, Institute of Molecule and Cell Biology, A*STAR, Singapore, Singapore.
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology (NUIST), Nanjing, Jiangsu Province, China.
| | - Soo Yong Tan
- Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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4
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Kuai H, Chen J, Tao X, Cai L, Imamura K, Matsumoto H, Liang P, Zhong N. Never-Ending Learning for Explainable Brain Computing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2307647. [PMID: 38602432 DOI: 10.1002/advs.202307647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/24/2024] [Indexed: 04/12/2024]
Abstract
Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge-driven forward inference and data-driven reverse inference, bolstered by the pre-trained language modeling techniques and the human-in-the-loop mechanisms. In particular, it incorporates internal evidence learning through multi-task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer-grained understanding of brain activity patterns related to human intelligence.
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Affiliation(s)
- Hongzhi Kuai
- Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, 100048, China
| | - Jianhui Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, 100124, China
| | - Xiaohui Tao
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, 4350, Australia
| | - Lingyun Cai
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, 100048, China
| | - Kazuyuki Imamura
- Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan
| | - Hiroki Matsumoto
- Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan
| | - Peipeng Liang
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, 100048, China
| | - Ning Zhong
- Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, 100048, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, 100124, China
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5
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Lysø EH, Hesjedal MB, Skolbekken JA, Solbjør M. Men's sociotechnical imaginaries of artificial intelligence for prostate cancer diagnostics - A focus group study. Soc Sci Med 2024; 347:116771. [PMID: 38537333 DOI: 10.1016/j.socscimed.2024.116771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Artificial intelligence (AI) is increasingly used for diagnostic purposes in cancer care. Prostate cancer is one of the most prevalent cancers affecting men worldwide, but current diagnostic approaches have limitations in terms of specificity and sensitivity. Using AI to interpret MR images in prostate cancer diagnostics shows promising results, but raises questions about implementation, user acceptance, trust, and doctor-patient communication. Drawing on approaches from the sociology of expectations and theories about sociotechnical imaginaries, we explore men's expectations of artificial intelligence for prostate cancer diagnostics. We conducted ten focus groups with 48 men aged 54-85 in Norway with various experiences of prostate cancer diagnostics. Five groups of men had been treated for prostate cancer, one group was on active surveillance, two groups had been through prostate cancer diagnostics without having a diagnosis, and two groups of men had no experience with prostate cancer diagnostics or treatment. Data was subject to reflexive thematic analysis. Our analysis suggests that men's expectations of AI for prostate cancer diagnostics come from two perspectives: Technology-centered expectations that build on their conceptions of AI's form and agency, and human-centered expectations of AI that build on their perceptions of patient-professional relationships and decision-making processes. These two perspectives are intertwined in three imaginaries of AI: The tool imaginary, the advanced machine imaginary, and the intelligence imaginary - each carrying distinct expectations and ideas of technologies and humans' role in decision-making processes. These expectations are multifaceted and simultaneously optimistic and pessimistic; while AI is expected to improve the accuracy of cancer diagnoses and facilitate more personalized medicine, AI is also expected to threaten interpersonal and communicational relationships between patients and healthcare professionals, and the maintenance of trust in these relationships. This emphasizes how AI cannot be implemented without caution about maintaining human healthcare relationships.
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Affiliation(s)
- Emilie Hybertsen Lysø
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway.
| | - Maria Bårdsen Hesjedal
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
| | - John-Arne Skolbekken
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
| | - Marit Solbjør
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
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De Boi I, Embrechts E, Schatteman Q, Penne R, Truijen S, Saeys W. Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression. Artif Intell Med 2024; 149:102770. [PMID: 38462272 DOI: 10.1016/j.artmed.2024.102770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 01/08/2024] [Accepted: 01/14/2024] [Indexed: 03/12/2024]
Abstract
Visuospatial neglect is a disorder characterised by impaired awareness for visual stimuli located in regions of space and frames of reference. It is often associated with stroke. Patients can struggle with all aspects of daily living and community participation. Assessment methods are limited and show several shortcomings, considering they are mainly performed on paper and do not implement the complexity of daily life. Similarly, treatment options are sparse and often show only small improvements. We present an artificial intelligence solution designed to accurately assess a patient's visuospatial neglect in a three-dimensional setting. We implement an active learning method based on Gaussian process regression to reduce the effort it takes a patient to undergo an assessment. Furthermore, we describe how this model can be utilised in patient oriented treatment and how this opens the way to gamification, tele-rehabilitation and personalised healthcare, providing a promising avenue for improving patient engagement and rehabilitation outcomes. To validate our assessment module, we conducted clinical trials involving patients in a real-world setting. We compared the results obtained using our AI-based assessment with the widely used conventional visuospatial neglect tests currently employed in clinical practice. The validation process serves to establish the accuracy and reliability of our model, confirming its potential as a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR application proves to be more sensitive, while intra-rater reliability remains high.
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Affiliation(s)
- Ivan De Boi
- Faculty of Applied Engineering, Department Electromechanics, Research Group InViLab, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium(1).
| | - Elissa Embrechts
- Department of Rehabilitation Sciences and Physiotherapy, Research Group MOVANT, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
| | - Quirine Schatteman
- Department of Rehabilitation Sciences and Physiotherapy, Research Group MOVANT, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
| | - Rudi Penne
- Faculty of Applied Engineering, Department Electromechanics, Research Group InViLab, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium(1)
| | - Steven Truijen
- Department of Rehabilitation Sciences and Physiotherapy, Research Group MOVANT, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
| | - Wim Saeys
- Department of Rehabilitation Sciences and Physiotherapy, Research Group MOVANT, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
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7
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Göndöcs D, Dörfler V. AI in medical diagnosis: AI prediction & human judgment. Artif Intell Med 2024; 149:102769. [PMID: 38462271 DOI: 10.1016/j.artmed.2024.102769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/02/2023] [Accepted: 01/14/2024] [Indexed: 03/12/2024]
Abstract
AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, 'explainability', and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.
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Affiliation(s)
| | - Viktor Dörfler
- University of Strathclyde Business School, United Kingdom.
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Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
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Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
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Sibbald M, Zwaan L, Yilmaz Y, Lal S. Incorporating artificial intelligence in medical diagnosis: A case for an invisible and (un)disruptive approach. J Eval Clin Pract 2024; 30:3-8. [PMID: 35761764 DOI: 10.1111/jep.13730] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 12/30/2022]
Abstract
As big data becomes more publicly accessible, artificial intelligence (AI) is increasingly available and applicable to problems around clinical decision-making. Yet the adoption of AI technology in healthcare lags well behind other industries. The gap between what technology could do, and what technology is actually being used for is rapidly widening. While many solutions are proposed to address this gap, clinician resistance to the adoption of AI remains high. To aid with change, we propose facilitating clinician decisions through technology by seamlessly weaving what we call 'invisible AI' into existing clinician workflows, rather than sequencing new steps into clinical processes. We explore evidence from the change management and human factors literature to conceptualize a new approach to AI implementation in health organizations. We discuss challenges and provide recommendations for organizations to employ this strategy.
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Affiliation(s)
- Matt Sibbald
- Department of Medicine, McMaster Education Research Innovation and Theory (MERIT) Program, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Laura Zwaan
- Erasmus Medical Center, Institute of Medical Education Research Rotterdam (iMERR), Rotterdam, The Netherlands
| | - Yusuf Yilmaz
- McMaster Education Research Innovation and Theory (MERIT) Program, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Continuing Professional Development Office, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Department of Medical Education, Faculty of Medicine, Ege University, Izmir, Turkey
| | - Sarrah Lal
- Department of Medicine, Division of Innovation and Education, McMaster University, Hamilton, ON, Canada
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10
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Liu J, Li D, Shan W, Liu S. Continual learning classification method with human-in-the-loop. MethodsX 2023; 11:102374. [PMID: 37753353 PMCID: PMC10518722 DOI: 10.1016/j.mex.2023.102374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 09/10/2023] [Indexed: 09/28/2023] Open
Abstract
The classification problem is essential to machine learning, often used in fault detection, condition monitoring, and behavior recognition. In recent years, due to the rapid development of incremental learning, reinforcement learning, transfer learning, and continual learning algorithms, the contradiction between the classification model and new data has been alleviated. However, due to the lack of feedback, most classification algorithms take long to search and may deviate from the correct results. Because of this, we propose a continual learning classification method with human-in-the-loop (H-CLCM) based on the artificial immune system. H-CLCM draws lessons from the mechanism that humans can enhance immune response through various intervention technologies and brings humans into the test learning process in a supervisory role. The human experience is integrated into the test phase, and the parameters corresponding to the error identification data are adjusted online. It enables it to converge to an accurate prediction model at the lowest cost and to learn new data categories without retraining the classifier.•All necessary steps and formulas of H-CLCM are provided.•H-CLCM adds manual intervention to improve the classification ability of the model.•H-CLCM can recognize new types of data.
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Affiliation(s)
- Jia Liu
- School of Petroleum and Natural Gas Engineering, Changzhou 213164, People's Republic of China
| | - Dong Li
- School of Petroleum and Natural Gas Engineering, Changzhou 213164, People's Republic of China
| | - Wangweiyi Shan
- School of Petroleum and Natural Gas Engineering, Changzhou 213164, People's Republic of China
| | - Shulin Liu
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, People's Republic of China
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11
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Shi M, Cheung G, Shahamiri SR. Speech and language processing with deep learning for dementia diagnosis: A systematic review. Psychiatry Res 2023; 329:115538. [PMID: 37864994 DOI: 10.1016/j.psychres.2023.115538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/23/2023]
Abstract
Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its progression or reduce its burdens. However, the diagnostic process of dementia is often complex and resource intensive. Access to diagnostic services is also an issue in low and middle-income countries. The abundance and easy accessibility of speech and language data have created new possibilities for utilizing Deep Learning (DL) technologies to be part of the dementia diagnostic process. This systematic review included studies published between 2012-2022 that utilized such technologies to aid in diagnosing dementia. We identified 72 studies using the PRISMA 2020 protocol, extracted and analyzed data from these studies and reported the related DL technologies. We found these technologies effectively differentiated between healthy individuals and those with a dementia diagnosis, highlighting their potential in the diagnosis of dementia. This systematic review provides insights into the contributions of DL-based speech and language techniques to support the dementia diagnostic process. It also offers an understanding of the advancements made in this field thus far and highlights some challenges that still need to be addressed.
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Affiliation(s)
- Mengke Shi
- Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand
| | - Gary Cheung
- Department of Psychological Medicine, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand
| | - Seyed Reza Shahamiri
- Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand.
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12
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Zhu Z, Lin K, Jain AK, Zhou J. Transfer Learning in Deep Reinforcement Learning: A Survey. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:13344-13362. [PMID: 37402188 PMCID: PMC11018366 DOI: 10.1109/tpami.2023.3292075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress.
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13
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Ajuwon BI, Awotundun ON, Richardson A, Roper K, Sheel M, Rahman N, Salako A, Lidbury BA. Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact. Int J Med Inform 2023; 179:105244. [PMID: 37820561 DOI: 10.1016/j.ijmedinf.2023.105244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 09/08/2023] [Accepted: 10/03/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND Machine learning (ML) prediction models to support clinical management of blood-borne viral infections are becoming increasingly abundant in medical literature, with a number of competing models being developed for the same outcome or target population. However, evidence on the quality of these ML prediction models are limited. OBJECTIVE This study aimed to evaluate the development and quality of reporting of ML prediction models that could facilitate timely clinical management of blood-borne viral infections. METHODS We conducted narrative evidence synthesis following the synthesis without meta-analysis guidelines. We searched PubMed and Cochrane Central Register of Controlled Trials for all studies applying ML models for predicting clinical outcomes associated with hepatitis B virus (HBV), human immunodeficiency virus (HIV), or hepatitis C virus (HCV). RESULTS We found 33 unique ML prediction models aiming to support clinical decision making. Overall, 12 (36.4%) focused on HBV, 10 (30.3%) on HCV, 10 on HIV (30.3%) and two (6.1%) on co-infection. Among these, six (18.2%) addressed the diagnosis of infection, 16 (48.5%) the prognosis of infection, eight (24.2%) the prediction of treatment response, two (6.1%) progression through a cascade of care, and one (3.03%) focused on the choice of antiretroviral therapy (ART). Nineteen prediction models (57.6%) were developed using data from high-income countries. Evaluation of prediction models was limited to measures of performance. Detailed information on software code accessibility was often missing. Independent validation on new datasets and/or in other institutions was rarely done. CONCLUSION Promising approaches for ML prediction models in blood-borne viral infections were identified, but the lack of robust validation, interpretability/explainability, and poor quality of reporting hampered their clinical relevance. Our findings highlight important considerations that can inform standard reporting guidelines for ML prediction models in the future (e.g., TRIPOD-AI), and provides critical data to inform robust evaluation of the models.
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Affiliation(s)
- Busayo I Ajuwon
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia; Department of Biosciences and Biotechnology, Faculty of Pure and Applied Sciences, Kwara State University, Malete, Nigeria.
| | - Oluwatosin N Awotundun
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Alice Richardson
- Statistical Support Network, The Australian National University, Acton, ACT, Australia
| | - Katrina Roper
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia
| | - Meru Sheel
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, Australia
| | - Nurudeen Rahman
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Abideen Salako
- Department of Clinical Sciences, Nigerian Institute of Medical Research, Yaba, Lagos State, Nigeria
| | - Brett A Lidbury
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia
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14
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Poulakis K, Westman E. Clustering and disease subtyping in Neuroscience, toward better methodological adaptations. Front Comput Neurosci 2023; 17:1243092. [PMID: 37927546 PMCID: PMC10620518 DOI: 10.3389/fncom.2023.1243092] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/04/2023] [Indexed: 11/07/2023] Open
Affiliation(s)
- Konstantinos Poulakis
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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15
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Kurvers RHJM, Nuzzolese AG, Russo A, Barabucci G, Herzog SM, Trianni V. Automating hybrid collective intelligence in open-ended medical diagnostics. Proc Natl Acad Sci U S A 2023; 120:e2221473120. [PMID: 37579152 PMCID: PMC10450668 DOI: 10.1073/pnas.2221473120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/05/2023] [Indexed: 08/16/2023] Open
Abstract
Collective intelligence has emerged as a powerful mechanism to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and medical diagnostics. However, collective intelligence has been mostly applied to relatively simple decision tasks (e.g., binary classifications). Applications in more open-ended tasks with a much larger problem space, such as emergency management or general medical diagnostics, are largely lacking, due to the challenge of integrating unstandardized inputs from different crowd members. Here, we present a fully automated approach for harnessing collective intelligence in the domain of general medical diagnostics. Our approach leverages semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology to overcome a major hurdle to collective intelligence in open-ended medical diagnostics, namely to identify the intended diagnosis from unstructured text. We tested our method on 1,333 medical cases diagnosed on a medical crowdsourcing platform: The Human Diagnosis Project. Each case was independently rated by ten diagnosticians. Comparing the diagnostic accuracy of single diagnosticians with the collective diagnosis of differently sized groups, we find that our method substantially increases diagnostic accuracy: While single diagnosticians achieved 46% accuracy, pooling the decisions of ten diagnosticians increased this to 76%. Improvements occurred across medical specialties, chief complaints, and diagnosticians' tenure levels. Our results show the life-saving potential of tapping into the collective intelligence of the global medical community to reduce diagnostic errors and increase patient safety.
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Affiliation(s)
- Ralf H. J. M. Kurvers
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin14191, Germany
- Science of Intelligence, Research Cluster of Excellence, Berlin10587, Germany
| | - Andrea Giovanni Nuzzolese
- Semantic Technology Laboratory & Collective Intelligence in Natural and Artificial Systems Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council, Rome00185, Italy
| | - Alessandro Russo
- Semantic Technology Laboratory & Collective Intelligence in Natural and Artificial Systems Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council, Rome00185, Italy
| | - Gioele Barabucci
- Norwegian University of Science and Technology, Trondheim7034, Norway
| | - Stefan M. Herzog
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin14191, Germany
| | - Vito Trianni
- Semantic Technology Laboratory & Collective Intelligence in Natural and Artificial Systems Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council, Rome00185, Italy
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16
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Holzinger A, Saranti A, Angerschmid A, Finzel B, Schmid U, Mueller H. Toward human-level concept learning: Pattern benchmarking for AI algorithms. PATTERNS (NEW YORK, N.Y.) 2023; 4:100788. [PMID: 37602217 PMCID: PMC10435961 DOI: 10.1016/j.patter.2023.100788] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Artificial intelligence (AI) today is very successful at standard pattern-recognition tasks due to the availability of large amounts of data and advances in statistical data-driven machine learning. However, there is still a large gap between AI pattern recognition and human-level concept learning. Humans can learn amazingly well even under uncertainty from just a few examples and are capable of generalizing these concepts to solve new conceptual problems. The growing interest in explainable machine intelligence requires experimental environments and diagnostic/benchmark datasets to analyze existing approaches and drive progress in pattern analysis and machine intelligence. In this paper, we provide an overview of current AI solutions for benchmarking concept learning, reasoning, and generalization; discuss the state-of-the-art of existing diagnostic/benchmark datasets (such as CLEVR, CLEVRER, CLOSURE, CURI, Bongard-LOGO, V-PROM, RAVEN, Kandinsky Patterns, CLEVR-Humans, CLEVRER-Humans, and their extension containing human language); and provide an outlook of some future research directions in this exciting research domain.
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Affiliation(s)
- Andreas Holzinger
- Human-Centered AI Lab, University of Natural Resources & Life Sciences Vienna, Vienna, Austria
- Medical University Graz, Graz, Austria
| | - Anna Saranti
- Human-Centered AI Lab, University of Natural Resources & Life Sciences Vienna, Vienna, Austria
- Medical University Graz, Graz, Austria
| | - Alessa Angerschmid
- Human-Centered AI Lab, University of Natural Resources & Life Sciences Vienna, Vienna, Austria
- Medical University Graz, Graz, Austria
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17
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Chen V, Bhatt U, Heidari H, Weller A, Talwalkar A. Perspectives on incorporating expert feedback into model updates. PATTERNS (NEW YORK, N.Y.) 2023; 4:100780. [PMID: 37521050 PMCID: PMC10382980 DOI: 10.1016/j.patter.2023.100780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration of how practitioners should translate domain expertise into ML updates. In this review, we consider how to capture interactions between practitioners and experts systematically. We devise a taxonomy to match expert feedback types with practitioner updates. A practitioner may receive feedback from an expert at the observation or domain level and then convert this feedback into updates to the dataset, loss function, or parameter space. We review existing work from ML and human-computer interaction to describe this feedback-update taxonomy and highlight the insufficient consideration given to incorporating feedback from non-technical experts. We end with a set of open questions that naturally arise from our proposed taxonomy and subsequent survey.
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Affiliation(s)
| | - Umang Bhatt
- University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | | | - Adrian Weller
- University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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18
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Wang Z, Lim G, Ng WY, Tan TE, Lim J, Lim SH, Foo V, Lim J, Sinisterra LG, Zheng F, Liu N, Tan GSW, Cheng CY, Cheung GCM, Wong TY, Ting DSW. Synthetic artificial intelligence using generative adversarial network for retinal imaging in detection of age-related macular degeneration. Front Med (Lausanne) 2023; 10:1184892. [PMID: 37425325 PMCID: PMC10324667 DOI: 10.3389/fmed.2023.1184892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 05/30/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction Age-related macular degeneration (AMD) is one of the leading causes of vision impairment globally and early detection is crucial to prevent vision loss. However, the screening of AMD is resource dependent and demands experienced healthcare providers. Recently, deep learning (DL) systems have shown the potential for effective detection of various eye diseases from retinal fundus images, but the development of such robust systems requires a large amount of datasets, which could be limited by prevalence of the disease and privacy of patient. As in the case of AMD, the advanced phenotype is often scarce for conducting DL analysis, which may be tackled via generating synthetic images using Generative Adversarial Networks (GANs). This study aims to develop GAN-synthesized fundus photos with AMD lesions, and to assess the realness of these images with an objective scale. Methods To build our GAN models, a total of 125,012 fundus photos were used from a real-world non-AMD phenotypical dataset. StyleGAN2 and human-in-the-loop (HITL) method were then applied to synthesize fundus images with AMD features. To objectively assess the quality of the synthesized images, we proposed a novel realness scale based on the frequency of the broken vessels observed in the fundus photos. Four residents conducted two rounds of gradings on 300 images to distinguish real from synthetic images, based on their subjective impression and the objective scale respectively. Results and discussion The introduction of HITL training increased the percentage of synthetic images with AMD lesions, despite the limited number of AMD images in the initial training dataset. Qualitatively, the synthesized images have been proven to be robust in that our residents had limited ability to distinguish real from synthetic ones, as evidenced by an overall accuracy of 0.66 (95% CI: 0.61-0.66) and Cohen's kappa of 0.320. For the non-referable AMD classes (no or early AMD), the accuracy was only 0.51. With the objective scale, the overall accuracy improved to 0.72. In conclusion, GAN models built with HITL training are capable of producing realistic-looking fundus images that could fool human experts, while our objective realness scale based on broken vessels can help identifying the synthetic fundus photos.
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Affiliation(s)
- Zhaoran Wang
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Gilbert Lim
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | - Wei Yan Ng
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Tien-En Tan
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Jane Lim
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Sing Hui Lim
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Valencia Foo
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Joshua Lim
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | | | - Feihui Zheng
- Singapore Eye Research Institute, Singapore, Singapore
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | - Gavin Siew Wei Tan
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Ching-Yu Cheng
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Gemmy Chui Ming Cheung
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
| | - Tien Yin Wong
- Singapore National Eye Centre, Singapore, Singapore
- School of Medicine, Tsinghua University, Beijing, China
| | - Daniel Shu Wei Ting
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Singapore National Eye Centre, Singapore, Singapore
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19
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Holzinger A, Keiblinger K, Holub P, Zatloukal K, Müller H. AI for life: Trends in artificial intelligence for biotechnology. N Biotechnol 2023; 74:16-24. [PMID: 36754147 DOI: 10.1016/j.nbt.2023.02.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/05/2023] [Accepted: 02/05/2023] [Indexed: 02/08/2023]
Abstract
Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.
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Affiliation(s)
- Andreas Holzinger
- University of Natural Resources and Life Sciences Vienna, Austria; Medical University Graz, Austria; Alberta Machine Intelligence Institute Edmonton, Canada.
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20
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Responsible and human centric AI-based insurance advisors. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2023.103273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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21
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Samaras AD, Moustakidis S, Apostolopoulos ID, Papandrianos N, Papageorgiou E. Classification models for assessing coronary artery disease instances using clinical and biometric data: an explainable man-in-the-loop approach. Sci Rep 2023; 13:6668. [PMID: 37095118 PMCID: PMC10125978 DOI: 10.1038/s41598-023-33500-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/13/2023] [Indexed: 04/26/2023] Open
Abstract
The main goal driving this work is to develop computer-aided classification models relying on clinical data to identify coronary artery disease (CAD) instances with high accuracy while incorporating the expert's opinion as input, making it a "man-in-the-loop" approach. CAD is traditionally diagnosed in a definite manner by Invasive Coronary Angiography (ICA). A dataset was created using biometric and clinical data from 571 patients (21 total features, 43% ICA-confirmed CAD instances) along with the expert's diagnostic yield. Five machine learning classification algorithms were applied to the dataset. For the selection of the best feature set for each algorithm, three different parameter selection algorithms were used. Each ML model's performance was evaluated using common metrics, and the best resulting feature set for each is presented. A stratified ten-fold validation was used for the performance evaluation. This procedure was run both using the assessments of experts/doctors as input and without them. The significance of this paper lies in its innovative approach of incorporating the expert's opinion as input in the classification process, making it a "man-in-the-loop" approach. This approach not only increases the accuracy of the models but also provides an added layer of explainability and transparency, allowing for greater trust and confidence in the results. Maximum achievable accuracy, sensitivity, and specificity are 83.02%, 90.32%, and 85.49% when using the expert's diagnosis as input, compared to 78.29%, 76.61%, and 86.07% without the expert's diagnosis. The results of this study demonstrate the potential for this approach to improve the diagnosis of CAD and highlight the importance of considering the role of human expertise in the development of computer-aided classification models.
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Affiliation(s)
| | - Serafeim Moustakidis
- Department of Energy Systems, University of Thessaly, Larisa, Greece.
- AIDEAS OÜ, Tallinn, Estonia.
| | - Ioannis D Apostolopoulos
- Department of Energy Systems, University of Thessaly, Larisa, Greece
- Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece
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22
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Innat M, Hossain MF, Mader K, Kouzani AZ. A convolutional attention mapping deep neural network for classification and localization of cardiomegaly on chest X-rays. Sci Rep 2023; 13:6247. [PMID: 37069168 PMCID: PMC10110554 DOI: 10.1038/s41598-023-32611-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 03/30/2023] [Indexed: 04/19/2023] Open
Abstract
Building a reliable and precise model for disease classification and identifying abnormal sites can provide physicians assistance in their decision-making process. Deep learning based image analysis is a promising technique for enriching the decision making process, and accordingly strengthening patient care. This work presents a convolutional attention mapping deep learning model, Cardio-XAttentionNet, to classify and localize cardiomegaly effectively. We revisit the global average pooling (GAP) system and add a weighting term to develop a light and effective Attention Mapping Mechanism (AMM). The model enables the classification of cardiomegaly from chest X-rays through image-level classification and pixel-level localization only from image-level labels. We leverage some of the advanced ConvNet architectures as a backbone-model of the proposed attention mapping network to build Cardio-XAttentionNet. The proposed model is trained on ChestX-Ray14, which is a publicly accessible chest X-ray dataset. The best single model achieves an overall precision, recall, F-1 measure and area under curve (AUC) scores of 0.87, 0.85, 0.86 and 0.89, respectively, for the classification of the cardiomegaly. The results also demonstrate that the Cardio-XAttentionNet model well captures the cardiomegaly class information at image-level as well as localization at pixel-level on chest x-rays. A comparative analysis between the proposed AMM and existing GAP based models shows that the proposed model achieves a state-of-the-art performance on this dataset for cardiomegaly detection using a single model.
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Affiliation(s)
- Mohammed Innat
- Department of Electronics and Communication Engineering, Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh
| | - Md Faruque Hossain
- Department of Electronics and Communication Engineering, Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh.
| | - Kevin Mader
- Institute for Biomedical Engineering, Swiss Federal Institute of Technology and University of Zurich, Zurich, Switzerland
| | - Abbas Z Kouzani
- School of Engineering, Deakin University, Waurn Ponds, Victoria, 3216, Australia.
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23
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Schobs LA, Swift AJ, Lu H. Uncertainty Estimation for Heatmap-Based Landmark Localization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1021-1034. [PMID: 36383596 DOI: 10.1109/tmi.2022.3222730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital component needed for these methods to be adopted in clinical settings, where it is imperative that erroneous predictions are caught and corrected. We propose Quantile Binning, a data-driven method to categorize predictions by uncertainty with estimated error bounds. Our framework can be applied to any continuous uncertainty measure, allowing straightforward identification of the best subset of predictions with accompanying estimated error bounds. We facilitate easy comparison between uncertainty measures by constructing two evaluation metrics derived from Quantile Binning. We compare and contrast three epistemic uncertainty measures (two baselines, and a proposed method combining aspects of the two), derived from two heatmap-based landmark localization model paradigms (U-Net and patch-based). We show results across three datasets, including a publicly available Cephalometric dataset. We illustrate how filtering out gross mispredictions caught in our Quantile Bins significantly improves the proportion of predictions under an acceptable error threshold. Finally, we demonstrate that Quantile Binning remains effective on landmarks with high aleatoric uncertainty caused by inherent landmark ambiguity, and offer recommendations on which uncertainty measure to use and how to use it. The code and data are available at https://github.com/schobs/qbin.
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van der Laan JJH, van der Putten JA, Zhao X, Karrenbeld A, Peters FTM, Westerhof J, de With PHN, van der Sommen F, Nagengast WB. Optical Biopsy of Dysplasia in Barrett's Oesophagus Assisted by Artificial Intelligence. Cancers (Basel) 2023; 15:cancers15071950. [PMID: 37046611 PMCID: PMC10093622 DOI: 10.3390/cancers15071950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Optical biopsy in Barrett's oesophagus (BE) using endocytoscopy (EC) could optimize endoscopic screening. However, the identification of dysplasia is challenging due to the complex interpretation of the highly detailed images. Therefore, we assessed whether using artificial intelligence (AI) as second assessor could help gastroenterologists in interpreting endocytoscopic BE images. First, we prospectively videotaped 52 BE patients with EC. Then we trained and tested the AI pm distinct datasets drawn from 83,277 frames, developed an endocytoscopic BE classification system, and designed online training and testing modules. We invited two successive cohorts for these online modules: 10 endoscopists to validate the classification system and 12 gastroenterologists to evaluate AI as second assessor by providing six of them with the option to request AI assistance. Training the endoscopists in the classification system established an improved sensitivity of 90.0% (+32.67%, p < 0.001) and an accuracy of 77.67% (+13.0%, p = 0.020) compared with the baseline. However, these values deteriorated at follow-up (-16.67%, p < 0.001 and -8.0%, p = 0.009). Contrastingly, AI-assisted gastroenterologists maintained high sensitivity and accuracy at follow-up, subsequently outperforming the unassisted gastroenterologists (+20.0%, p = 0.025 and +12.22%, p = 0.05). Thus, best diagnostic scores for the identification of dysplasia emerged through human-machine collaboration between trained gastroenterologists with AI as the second assessor. Therefore, AI could support clinical implementation of optical biopsies through EC.
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Affiliation(s)
- Jouke J H van der Laan
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Joost A van der Putten
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Xiaojuan Zhao
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Arend Karrenbeld
- Department of Pathology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Frans T M Peters
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Jessie Westerhof
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Wouter B Nagengast
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
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Yang CY, Shiranthika C, Wang CY, Chen KW, Sumathipala S. Reinforcement learning strategies in cancer chemotherapy treatments: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107280. [PMID: 36529000 DOI: 10.1016/j.cmpb.2022.107280] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Cancer is one of the major causes of death worldwide and chemotherapies are the most significant anti-cancer therapy, in spite of the emerging precision cancer medicines in the last 2 decades. The growing interest in developing the effective chemotherapy regimen with optimal drug dosing schedule to benefit the clinical cancer patients has spawned innovative solutions involving mathematical modeling since the chemotherapy regimens are administered cyclically until the futility or the occurrence of intolerable adverse events. Thus, in this present work, we reviewed the emerging trends involved in forming a computational solution from the aspect of reinforcement learning. METHODS Initially, this survey in-depth focused on the details of the dynamic treatment regimens from a broad perspective and then narrowed down to inspirations from reinforcement learning that were advantageous to chemotherapy dosing, including both offline reinforcement learning and supervised reinforcement learning. RESULTS The insights established in the chemotherapy-planning problem associated with the Reinforcement Learning (RL) has been discussed in this study. It showed that the researchers were able to widen their perspectives in comprehending the theoretical basis, dynamic treatment regimens (DTR), use of the adaptive control on DTR, and the associated RL techniques. CONCLUSIONS This study reviewed the recent researches relevant to the topic, and highlighted the challenges, open questions, possible solutions, and future steps in inventing a realistic solution for the aforementioned problem.
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Affiliation(s)
- Chan-Yun Yang
- Department of Electrical Engineering, National Taipei University, New Taipei City, Taiwan
| | - Chamani Shiranthika
- Department of Electrical Engineering, National Taipei University, New Taipei City, Taiwan
| | - Chung-Yih Wang
- Department of Radiation Oncology, Cheng Hsin General Hospital, Taipei City, Taiwan
| | - Kuo-Wei Chen
- Section of Hematology and Oncology, Department of Internal Medicine, Cheng Hsin General Hospital, Taipei City, Taiwan.
| | - Sagara Sumathipala
- Faculty of Information Technology, University of Moratuwa, Katubedda, Moratuwa, Sri Lanka
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26
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Krake T, Klotzl D, Eberhardt B, Weiskopf D. Constrained Dynamic Mode Decomposition. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:182-192. [PMID: 36170398 DOI: 10.1109/tvcg.2022.3209437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Frequency-based decomposition of time series data is used in many visualization applications. Most of these decomposition methods (such as Fourier transform or singular spectrum analysis) only provide interaction via pre- and post-processing, but no means to influence the core algorithm. A method that also belongs to this class is Dynamic Mode Decomposition (DMD), a spectral decomposition method that extracts spatio-temporal patterns from data. In this paper, we incorporate frequency-based constraints into DMD for an adaptive decomposition that leads to user-controllable visualizations, allowing analysts to include their knowledge into the process. To accomplish this, we derive an equivalent reformulation of DMD that implicitly provides access to the eigenvalues (and therefore to the frequencies) identified by DMD. By utilizing a constrained minimization problem customized to DMD, we can guarantee the existence of desired frequencies by minimal changes to DMD. We complement this core approach by additional techniques for constrained DMD to facilitate explorative visualization and investigation of time series data. With several examples, we demonstrate the usefulness of constrained DMD and compare it to conventional frequency-based decomposition methods.
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Bull NJ, Honan B, Spratt NJ, Quilty S. A method for rapid machine learning development for data mining with doctor-in-the-loop. PLoS One 2023; 18:e0284965. [PMID: 37163511 PMCID: PMC10171605 DOI: 10.1371/journal.pone.0284965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/13/2023] [Indexed: 05/12/2023] Open
Abstract
Classifying free-text from historical databases into research-compatible formats is a barrier for clinicians undertaking audit and research projects. The aim of this study was to (a) develop interactive active machine-learning model training methodology using readily available software that was (b) easily adaptable to a wide range of natural language databases and allowed customised researcher-defined categories, and then (c) evaluate the accuracy and speed of this model for classifying free text from two unique and unrelated clinical notes into coded data. A user interface for medical experts to train and evaluate the algorithm was created. Data requiring coding in the form of two independent databases of free-text clinical notes, each of unique natural language structure. Medical experts defined categories relevant to research projects and performed 'label-train-evaluate' loops on the training data set. A separate dataset was used for validation, with the medical experts blinded to the label given by the algorithm. The first dataset was 32,034 death certificate records from Northern Territory Births Deaths and Marriages, which were coded into 3 categories: haemorrhagic stroke, ischaemic stroke or no stroke. The second dataset was 12,039 recorded episodes of aeromedical retrieval from two prehospital and retrieval services in Northern Territory, Australia, which were coded into 5 categories: medical, surgical, trauma, obstetric or psychiatric. For the first dataset, macro-accuracy of the algorithm was 94.7%. For the second dataset, macro-accuracy was 92.4%. The time taken to develop and train the algorithm was 124 minutes for the death certificate coding, and 144 minutes for the aeromedical retrieval coding. This machine-learning training method was able to classify free-text clinical notes quickly and accurately from two different health datasets into categories of relevance to clinicians undertaking health service research.
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Affiliation(s)
- Neva J Bull
- School of Psychological Sciences, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, John Hunter Hospital, New Lambton Heights, NSW, Australia
| | - Bridget Honan
- Alice Springs Hospital, Alice Springs, NT, Australia
| | - Neil J Spratt
- Hunter Medical Research Institute, John Hunter Hospital, New Lambton Heights, NSW, Australia
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Department of Neurology, John Hunter Hospital, New Lambton Heights, NSW, Australia
| | - Simon Quilty
- Alice Springs Hospital, Alice Springs, NT, Australia
- National Centre of Epidemiology and Population Health, Australian National University, Canberra, ACT, Australia
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28
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Parimbelli E, Buonocore TM, Nicora G, Michalowski W, Wilk S, Bellazzi R. Why did AI get this one wrong? - Tree-based explanations of machine learning model predictions. Artif Intell Med 2023; 135:102471. [PMID: 36628785 DOI: 10.1016/j.artmed.2022.102471] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to interpret and explain, culminating in black-box machine learning models. Model developers and users alike are often presented with a trade-off between performance and intelligibility, especially in high-stakes applications like medicine. In the present article we propose a novel methodological approach for generating explanations for the predictions of a generic machine learning model, given a specific instance for which the prediction has been made. The method, named AraucanaXAI, is based on surrogate, locally-fitted classification and regression trees that are used to provide post-hoc explanations of the prediction of a generic machine learning model. Advantages of the proposed XAI approach include superior fidelity to the original model, ability to deal with non-linear decision boundaries, and native support to both classification and regression problems. We provide a packaged, open-source implementation of the AraucanaXAI method and evaluate its behaviour in a number of different settings that are commonly encountered in medical applications of AI. These include potential disagreement between the model prediction and physician's expert opinion and low reliability of the prediction due to data scarcity.
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Affiliation(s)
- Enea Parimbelli
- Department of Electric, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy; Telfer school of Management, University of Ottawa, Ottawa, Ontario, Canada.
| | - Tommaso Mario Buonocore
- Department of Electric, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Giovanna Nicora
- Department of Electric, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy; enGenome srl, Pavia, Italy
| | - Wojtek Michalowski
- Telfer school of Management, University of Ottawa, Ottawa, Ontario, Canada
| | - Szymon Wilk
- Division of Intelligent Decision Support Systems, Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Riccardo Bellazzi
- Department of Electric, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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29
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Nusir M, Rekik M. Systematic review of co-design in digital health for COVID-19 research. UNIVERSAL ACCESS IN THE INFORMATION SOCIETY 2022:1-15. [PMID: 36618758 PMCID: PMC9805349 DOI: 10.1007/s10209-022-00964-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Improving the quality of digital health care through information and communication technology can mainly contribute to the clinical, social, financial, and economic systems' success, especially during the COVID-19 pandemic period. The co-design approach, which unleashes the end-user power, can contribute actively in improving the healthcare systems. It deals with understanding the user behaviors, requirements, and motivations through observation, inspection, task analysis, and feedback techniques. Consequently, both the co-design and digital technologies might empower the management of patients' health and that of their families. The research strategy is based on a systematic literature review and meta-analysis to summarize how the co-design methodologies handled the existing technology-based health systems for their improvement. Based on the findings, we establish the following hypotheses: (i) A user-centered methodology for service implementation might offer a promising tool to enhance the healthcare services quality before they be launched; (ii) Several limitations can affect the co-design approach in digital health, such as a bias for a patients' group. Efforts have been made to reduce this risk by identifying bias at an early stage, or different groups should be included in the test phase for example; (iii) Use decision-making devices that handle technologies for patient and clinical healthcare solution.
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Affiliation(s)
- Muneer Nusir
- Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, 16278 Saudi Arabia
| | - Molka Rekik
- Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, 16278 Saudi Arabia
- Data Engineering and Semantics Research Unit, University of Sfax, Sfax, Tunisia
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30
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Information Visualisation for Antibiotic Detection Biochip Design and Testing. Processes (Basel) 2022. [DOI: 10.3390/pr10122680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Biochips are engineered substrates that have different spots that change colour according to biochemical reactions. These spots can be read together to detect different analytes (such as different types of antibiotic, pathogens, or biological agents). While some chips are designed so that each spot on its own can detect a particular analyte, chip designs that use a combination of spots to detect different analytes can be more efficient and detect a larger number of analytes with a smaller number of spots. These types of chip can, however, be more difficult to design, as an efficient and effective combination of biosensors needs to be selected for the chip. These need to be able to differentiate between a range of different analytes so the values can be combined in a way that demonstrates the confidence that a particular analyte is present or not. The study described in this paper examines the potential for information visualisation to support the process of designing and reading biochips by developing and evaluating applications that allow biologists to analyse the results of experiments aimed at detecting candidate bio-sensors (to be used as biochip spots) and examining how biosensors can combine to identify different analytes. Our results demonstrate the potential of information visualisation and machine learning techniques to improve the design of biochips.
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31
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Schmid U, Wrede B. What is Missing in XAI So Far? KUNSTLICHE INTELLIGENZ 2022. [DOI: 10.1007/s13218-022-00786-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AbstractWith the perspective on applications of AI-technology, especially data intensive deep learning approaches, the need for methods to control and understand such models has been recognized and gave rise to a new research domain labeled explainable artificial intelligence (XAI). In this overview paper we give an interim appraisal of what has been achieved so far and where there are still gaps in the research. We take an interdisciplinary perspective to identify challenges on XAI research and point to open questions with respect to the quality of the explanations regarding faithfulness and consistency of explanations. On the other hand we see a need regarding the interaction between XAI and user to allow for adaptability to specific information needs and explanatory dialog for informed decision making as well as the possibility to correct models and explanations by interaction. This endeavor requires an integrated interdisciplinary perspective and rigorous approaches to empirical evaluation based on psychological, linguistic and even sociological theories.
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32
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Farahani FV, Fiok K, Lahijanian B, Karwowski W, Douglas PK. Explainable AI: A review of applications to neuroimaging data. Front Neurosci 2022; 16:906290. [PMID: 36583102 PMCID: PMC9793854 DOI: 10.3389/fnins.2022.906290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Deep neural networks (DNNs) have transformed the field of computer vision and currently constitute some of the best models for representations learned via hierarchical processing in the human brain. In medical imaging, these models have shown human-level performance and even higher in the early diagnosis of a wide range of diseases. However, the goal is often not only to accurately predict group membership or diagnose but also to provide explanations that support the model decision in a context that a human can readily interpret. The limited transparency has hindered the adoption of DNN algorithms across many domains. Numerous explainable artificial intelligence (XAI) techniques have been developed to peer inside the "black box" and make sense of DNN models, taking somewhat divergent approaches. Here, we suggest that these methods may be considered in light of the interpretation goal, including functional or mechanistic interpretations, developing archetypal class instances, or assessing the relevance of certain features or mappings on a trained model in a post-hoc capacity. We then focus on reviewing recent applications of post-hoc relevance techniques as applied to neuroimaging data. Moreover, this article suggests a method for comparing the reliability of XAI methods, especially in deep neural networks, along with their advantages and pitfalls.
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Affiliation(s)
- Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Krzysztof Fiok
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Behshad Lahijanian
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Waldemar Karwowski
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Pamela K. Douglas
- School of Modeling, Simulation, and Training, University of Central Florida, Orlando, FL, United States
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33
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Jarrahi MH, Davoudi V, Haeri M. The key to an effective AI-powered digital pathology: Establishing a symbiotic workflow between pathologists and machine. J Pathol Inform 2022; 13:100156. [PMID: 36605113 PMCID: PMC9808012 DOI: 10.1016/j.jpi.2022.100156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022] Open
Abstract
Pathology is a fundamental element of modern medicine that determines the final diagnosis of medical conditions, leads medical decisions, and portrays the prognosis. Due to continuous improvements in AI capabilities (e.g., object recognition and image processing), intelligent systems are bound to play a key role in augmenting pathology research and clinical practices. Despite the pervasive deployment of computational approaches in similar fields such as radiology, there has been less success in integrating AI in clinical practices and histopathological diagnosis. This is partly due to the opacity of end-to-end AI systems, which raises issues of interoperability and accountability of medical practices. In this article, we draw on interactive machine learning to take advantage of AI in digital pathology to open the black box of AI and generate a more effective partnership between pathologists and AI systems based on the metaphors of parameterization and implicitization.
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Affiliation(s)
- Mohammad Hossein Jarrahi
- University of North Carolina, 100 Manning Hall, Chapel Hill, NC 27599, USA
- Corresponding authors.
| | - Vahid Davoudi
- Alzheimer Disease Research Center, University of Kansas, Kansas University Medical Center, Kansas City, Kansas, USA
| | - Mohammad Haeri
- Alzheimer Disease Research Center, University of Kansas, Kansas University Medical Center, Kansas City, Kansas, USA
- Department of Pathology & Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
- Corresponding authors.
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Banham A, Leemans SJJ, Wynn MT, Andrews R, Laupland KB, Shinners L. xPM: Enhancing exogenous data visibility. Artif Intell Med 2022; 133:102409. [PMID: 36328672 DOI: 10.1016/j.artmed.2022.102409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 12/13/2022]
Abstract
Process mining is a well-established discipline with applications in many industry sectors, including healthcare. To date, few publications have considered the context in which processes execute. Little consideration has been given as to how contextual data (exogenous data) can be practically included for process mining analysis, beyond including case or event attributes in a typical event log. We show that the combination of process data (endogenous) and exogenous data can generate insights not possible with standard process mining techniques. Our contributions are a framework for process mining with exogenous data and new analyses, where exogenous data and process behaviour are linked to process outcomes. Our new analyses visualise exogenous data, highlighting the trends and variations, to show where overlaps or distinctions exist between outcomes. We applied our analyses in a healthcare setting and show that clinicians could extract insights about differences in patients' vital signs (exogenous data) relevant to clinical outcomes. We present two evaluations, using a publicly available data set, MIMIC-III, to demonstrate the applicability of our analysis. These evaluations show that process mining can integrate large amounts of physiologic data and interventions, with resulting discrimination and conversion to clinically interpretable information.
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Affiliation(s)
- Adam Banham
- Queensland University of Technology, Brisbane, Queensland, Australia.
| | | | - Moe T Wynn
- Queensland University of Technology, Brisbane, Queensland, Australia
| | - Robert Andrews
- Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kevin B Laupland
- Queensland University of Technology, Brisbane, Queensland, Australia; Department of Intensive Care Services, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Lucy Shinners
- Southern Cross University, Bilinga, Queensland, Australia
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Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95:20220239. [PMID: 35867841 PMCID: PMC9793488 DOI: 10.1259/bjr.20220239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Sunan Cui
- Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA
| | - Julia Pakela
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Yi Luo
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | | | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
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36
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Berndsen J, McHugh D. Comment on “Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care”. Sports Med 2022. [DOI: 10.1007/s40279-022-01770-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Mitra A, Jain A, Kishore A, Kumar P. A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach. OPERATIONS RESEARCH FORUM 2022. [PMCID: PMC9514716 DOI: 10.1007/s43069-022-00166-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Demand forecasting has been a major concern of operational strategy to manage the inventory and optimize the customer satisfaction level. The researchers have proposed many conventional and advanced forecasting techniques, but no one leads to complete accuracy. Forecasting is equally important in manufacturing as well as retail companies. In this study, the performances of five regression techniques of machine learning, viz. random forest (RF), extreme gradient boosting (XGBoost), gradient boosting, adaptive boosting (AdaBoost), and artificial neural network (ANN) algorithms, are compared with a proposed hybrid (RF-XGBoost-LR) model for sales forecasting of a retail chain considering the various parameters of forecasting accuracy. The weekly sales data of a US-based retail company is considered in the analysis of the forecasts undertaking the attributes affecting the sale such as the temperature of the region and the size of the store. It is observed that the hybrid RF-XGBoost-LR outperformed the other models measured against various metrics of performance. This study may help the industry decision-maker to understand and improve the methods of forecasting.
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Affiliation(s)
- Arnab Mitra
- Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042 India
| | - Arnav Jain
- Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042 India
| | - Avinash Kishore
- Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042 India
| | - Pravin Kumar
- Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042 India
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Baumeister H, Garatva P, Pryss R, Ropinski T, Montag C. Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung? PSYCHOLOGISCHE RUNDSCHAU 2022. [DOI: 10.1026/0033-3042/a000609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Zusammenfassung. Digitale Phänotypisierung stellt einen neuen, leistungsstarken Ansatz zur Realisierung psychodiagnostischer Aufgaben in vielen Bereichen der Psychologie und Medizin dar. Die Grundidee besteht aus der Nutzung digitaler Spuren aus dem Alltag, um deren Vorhersagekraft für verschiedenste Anwendungsmöglichkeiten zu überprüfen und zu nutzen. Voraussetzungen für eine erfolgreiche Umsetzung sind elaborierte Smart Sensing Ansätze sowie Big Data-basierte Extraktions- (Data Mining) und Machine Learning-basierte Analyseverfahren. Erste empirische Studien verdeutlichen das hohe Potential, aber auch die forschungsmethodischen sowie ethischen und rechtlichen Herausforderungen, um über korrelative Zufallsbefunde hinaus belastbare Befunde zu gewinnen. Hierbei müssen rechtliche und ethische Richtlinien sicherstellen, dass die Erkenntnisse in einer für Einzelne und die Gesellschaft als Ganzes wünschenswerten Weise genutzt werden. Für die Psychologie als Lehr- und Forschungsdomäne bieten sich durch Digitale Phänotypisierung vielfältige Möglichkeiten, die zum einen eine gelebte Zusammenarbeit verschiedener Fachbereiche und zum anderen auch curriculare Erweiterungen erfordern. Die vorliegende narrative Übersicht bietet eine theoretische, nicht-technische Einführung in das Forschungsfeld der Digitalen Phänotypisierung, mit ersten empirischen Befunden sowie einer Diskussion der Möglichkeiten und Grenzen sowie notwendigen Handlungsfeldern.
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Affiliation(s)
- Harald Baumeister
- Abteilung für Klinische Psychologie und Psychotherapie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| | - Patricia Garatva
- Abteilung für Klinische Psychologie und Psychotherapie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| | - Rüdiger Pryss
- Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Deutschland
| | - Timo Ropinski
- Arbeitsgruppe Visual Computing, Institut für Medieninformatik, Universität Ulm, Deutschland
| | - Christian Montag
- Abteilung für Molekulare Psychologie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
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Detection of sleep apnea from single-channel electroencephalogram (EEG) using an explainable convolutional neural network (CNN). PLoS One 2022; 17:e0272167. [PMID: 36099242 PMCID: PMC9469966 DOI: 10.1371/journal.pone.0272167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 06/21/2022] [Indexed: 11/19/2022] Open
Abstract
Sleep apnea (SA) is a common disorder involving the cessation of breathing during sleep. It can cause daytime hypersomnia, accidents, and, if allowed to progress, serious, chronic conditions. Continuous positive airway pressure is an effective SA treatment. However, long waitlists impede timely diagnosis; overnight sleep studies involve trained technicians scoring a polysomnograph, which comprises multiple physiological signals including multi-channel electroencephalography (EEG). Therefore, it is important to develop simplified and automated approaches to detect SA. In the present study, we have developed an explainable convolutional neural network (CNN) to detect SA events from single-channel EEG recordings which generalizes across subjects. The network architecture consisted of three convolutional layers. We tuned hyperparameters using the Hyperband algorithm, optimized parameters using Adam, and quantified network performance with subjectwise 10-fold cross-validation. Our CNN performed with an accuracy of 69.9%, and a Matthews correlation coefficient (MCC) of 0.38. To explain the mechanisms of our trained network, we used critical-band masking (CBM): after training, we added bandlimited noise to test recordings; we parametrically varied the noise band center frequency and noise intensity, quantifying the deleterious effect on performance. We reconciled the effects of CBM with lesioning, wherein we zeroed the trained network’s 1st-layer filter kernels in turn, quantifying the deleterious effect on performance. These analyses indicated that the network learned frequency-band information consistent with known SA biomarkers, specifically, delta and beta band activity. Our results indicate single-channel EEG may have clinical potential for SA diagnosis.
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Gottlieb ER, Samuel M, Bonventre JV, Celi LA, Mattie H. Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit. Adv Chronic Kidney Dis 2022; 29:431-438. [PMID: 36253026 PMCID: PMC9586459 DOI: 10.1053/j.ackd.2022.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/01/2022] [Accepted: 06/22/2022] [Indexed: 01/25/2023]
Abstract
Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.
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Affiliation(s)
- Eric R Gottlieb
- Renal Section, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA.
| | | | - Joseph V Bonventre
- Renal Section, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Leo A Celi
- Harvard Medical School, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; MIT Critical Data, Cambridge, MA; Harvard T.H. Chan School of Public Health, Boston, MA; Beth Israel Deaconess Medical Center, Boston, MA
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41
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Li X, Liu X, Deng X, Fan Y. Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction. Biomedicines 2022; 10:2157. [PMID: 36140258 PMCID: PMC9495955 DOI: 10.3390/biomedicines10092157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/26/2022] [Accepted: 08/28/2022] [Indexed: 11/16/2022] Open
Abstract
Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML-based models to directly predict CVD based on risk factors or medical imaging findings and the ML-based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data-driven models and physics-driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.
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Affiliation(s)
- Xiaoyin Li
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Xiao Liu
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Xiaoyan Deng
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Yubo Fan
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
- School of Engineering Medicine, Beihang University, Beijing 100083, China
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Abstract
AbstractResearchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control; interactive machine learning, in which there is a closer interaction between users and learning systems; and machine teaching, where human domain experts have control over the learning process. Aside from control, humans can also be involved in the learning process in other ways. In curriculum learning human domain experts try to impose some structure on the examples presented to improve the learning; in explainable AI the focus is on the ability of the model to explain to humans why a given solution was chosen. This collaboration between AI models and humans should not be limited only to the learning process; if we go further, we can see other terms that arise such as Usable and Useful AI. In this paper we review the state of the art of the techniques involved in the new forms of relationship between humans and ML algorithms. Our contribution is not merely listing the different approaches, but to provide definitions clarifying confusing, varied and sometimes contradictory terms; to elucidate and determine the boundaries between the different methods; and to correlate all the techniques searching for the connections and influences between them.
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Fairness-Aware Predictive Graph Learning in Social Networks. MATHEMATICS 2022. [DOI: 10.3390/math10152696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Predictive graph learning approaches have been bringing significant advantages in many real-life applications, such as social networks, recommender systems, and other social-related downstream tasks. For those applications, learning models should be able to produce a great prediction result to maximize the usability of their application. However, the paradigm of current graph learning methods generally neglects the differences in link strength, leading to discriminative predictive results, resulting in different performance between tasks. Based on that problem, a fairness-aware predictive learning model is needed to balance the link strength differences and not only consider how to formulate it. To address this problem, we first formally define two biases (i.e., Preference and Favoritism) that widely exist in previous representation learning models. Then, we employ modularity maximization to distinguish strong and weak links from the quantitative perspective. Eventually, we propose a novel predictive learning framework entitled ACE that first implements the link strength differentiated learning process and then integrates it with a dual propagation process. The effectiveness and fairness of our proposed ACE have been verified on four real-world social networks. Compared to nine different state-of-the-art methods, ACE and its variants show better performance. The ACE framework can better reconstruct networks, thus also providing a high possibility of resolving misinformation in graph-structured data.
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Orjuela-Cañón AD, Jutinico AL, Awad C, Vergara E, Palencia A. Machine learning in the loop for tuberculosis diagnosis support. Front Public Health 2022; 10:876949. [PMID: 35958865 PMCID: PMC9362992 DOI: 10.3389/fpubh.2022.876949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.
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Affiliation(s)
- Alvaro D. Orjuela-Cañón
- School of Medicine and Health Sciences, Universidad del Rosario, Bogotá, Colombia
- *Correspondence: Alvaro D. Orjuela-Cañón
| | | | - Carlos Awad
- Subred Integrada de Servicios de Salud Centro Oriente E.S.E, Bogotá, Colombia
| | - Erika Vergara
- Biomedical Engineering, Universidad Antonio Nariño, Bogotá, Colombia
| | - Angélica Palencia
- Subred Integrada de Servicios de Salud Centro Oriente E.S.E, Bogotá, Colombia
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Garcia Santa Cruz B, Slter J, Gomez-Giro G, Saraiva C, Sabate-Soler S, Modamio J, Barmpa K, Schwamborn JC, Hertel F, Jarazo J, Husch A. Generalising from conventional pipelines using deep learning in high-throughput screening workflows. Sci Rep 2022; 12:11465. [PMID: 35794231 PMCID: PMC9259641 DOI: 10.1038/s41598-022-15623-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 06/27/2022] [Indexed: 11/09/2022] Open
Abstract
The study of complex diseases relies on large amounts of data to build models toward precision medicine. Such data acquisition is feasible in the context of high-throughput screening, in which the quality of the results relies on the accuracy of the image analysis. Although state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manually generating ground truth labels for model training hampers the day-to-day application in experimental laboratories. Alternatively, traditional computer vision-based solutions do not need expensive labels for their implementation. Our work combines both approaches by training a deep learning network using weak training labels automatically generated with conventional computer vision methods. Our network surpasses the conventional segmentation quality by generalising beyond noisy labels, providing a 25% increase of mean intersection over union, and simultaneously reducing the development and inference times. Our solution was embedded into an easy-to-use graphical user interface that allows researchers to assess the predictions and correct potential inaccuracies with minimal human input. To demonstrate the feasibility of training a deep learning solution on a large dataset of noisy labels automatically generated by a conventional pipeline, we compared our solution against the common approach of training a model from a small manually curated dataset by several experts. Our work suggests that humans perform better in context interpretation, such as error assessment, while computers outperform in pixel-by-pixel fine segmentation. Such pipelines are illustrated with a case study on image segmentation for autophagy events. This work aims for better translation of new technologies to real-world settings in microscopy-image analysis.
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Affiliation(s)
- Beatriz Garcia Santa Cruz
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 4, Rue Ernest Barble, 1210, Luxembourg (City), Luxembourg. .,Interventional Neuroscience Group, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg.
| | - Jan Slter
- Interventional Neuroscience Group, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Gemma Gomez-Giro
- Developmental and Cellular Biology, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Claudia Saraiva
- Developmental and Cellular Biology, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Sonia Sabate-Soler
- Developmental and Cellular Biology, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Jennifer Modamio
- Developmental and Cellular Biology, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Kyriaki Barmpa
- Developmental and Cellular Biology, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Jens Christian Schwamborn
- Developmental and Cellular Biology, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 4, Rue Ernest Barble, 1210, Luxembourg (City), Luxembourg.,Interventional Neuroscience Group, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Javier Jarazo
- Developmental and Cellular Biology, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg.,OrganoTherapeutics SARL, 6A, avenue des Hauts-Fourneaux, 4365, Esch-sur-Alzette, Luxembourg
| | - Andreas Husch
- Interventional Neuroscience Group, Luxembourg Center for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg. .,Systems Control Group, Luxembourg Centere for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, 4367, Belvaux, Luxembourg.
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Thun LJ, Teh PL, Cheng CB. CyberAid: Are your children safe from cyberbullying? JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2021.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Ono S, Goto T. Introduction to supervised machine learning in clinical epidemiology. ANNALS OF CLINICAL EPIDEMIOLOGY 2022; 4:63-71. [PMID: 38504945 PMCID: PMC10760492 DOI: 10.37737/ace.22009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Machine learning refers to a series of processes in which a computer finds rules from a vast amount of data. With recent advances in computer technology and the availability of a wide variety of health data, machine learning has rapidly developed and been applied in medical research. Currently, there are three types of machine learning: supervised, unsupervised, and reinforcement learning. In medical research, supervised learning is commonly used for diagnoses and prognoses, while unsupervised learning is used for phenotyping a disease, and reinforcement learning for maximizing favorable results, such as optimization of total patients' waiting time in the emergency department. The present article focuses on the concept and application of supervised learning in medicine, the most commonly used machine learning approach in medicine, and provides a brief explanation of four algorithms widely used for prediction (random forests, gradient-boosted decision tree, support vector machine, and neural network). Among these algorithms, the neural network has further developed into deep learning algorithms to solve more complex tasks. Along with simple classification problems, deep learning is commonly used to process medical imaging, such as retinal fundus photographs for diabetic retinopathy diagnosis. Although machine learning can bring new insights into medicine by processing a vast amount of data that are often beyond human capacity, algorithms can also fail when domain knowledge is neglected. The combination of algorithms and human cognitive ability is a key to the successful application of machine learning in medicine.
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Affiliation(s)
- Sachiko Ono
- Department of Eat-loss Medicine, Graduate School of Medicine, The University of Tokyo
| | - Tadahiro Goto
- Department of Clinical Epidemiology and Health Economics, The University of Tokyo
- TXP Medical Co. Ltd
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48
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Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution. SUSTAINABILITY 2022. [DOI: 10.3390/su14137804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Artificial intelligence (AI) is currently being developed by large corporations, and governments all over the world are yearning for it. AI isn’t a futuristic concept; it is already here, and it is being implemented in a range of industries. Finance, national security, health care, criminal justice, transportation, and smart cities are all examples of this. There are countless examples of AI having a substantial impact on the world and complementing human abilities. However, due to the immense societal ramifications of these technologies, AI is on the verge of disrupting a host of industries, so the technique by which AI systems are created must be better understood. The goal of the study was to look at what it meant to be human-centred, how to create human-centred AI, and what considerations should be made for human-centred AI to achieve sustainability and the SDGs. Using a systematic literature review technique, the study discovered that a human-centred AI strategy strives to create and implement AI systems in ways that benefit mankind and serve their interests. The study also found that a human-in-the-loop concept should be used to develop procedures for creating human-centred AI, as well as other initiatives, such as the promotion of AI accountability, encouraging businesses to use autonomy wisely, to motivate businesses to be aware of human and algorithmic biases, to ensure that businesses prioritize customers, and form multicultural teams to tackle AI research. The study concluded with policy recommendations for human-centred AI to help accomplish the SDGs, including expanding government AI investments, addressing data and algorithm biases, and resolving data access issues, among other things.
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Ambale-Venkatesh B, Lima JAC. Human-in-the-Loop Artificial Intelligence in Cardiac MRI. Radiology 2022; 305:80-81. [PMID: 35699584 DOI: 10.1148/radiol.221132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Bharath Ambale-Venkatesh
- From the Department of Radiology (B.A.V.) and School of Medicine (J.A.C.L.), Johns Hopkins University, 600 N Wolfe St, MR 110, Baltimore, MD 21287
| | - João A C Lima
- From the Department of Radiology (B.A.V.) and School of Medicine (J.A.C.L.), Johns Hopkins University, 600 N Wolfe St, MR 110, Baltimore, MD 21287
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50
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Lughofer E. Evolving multi-user fuzzy classifier systems integrating human uncertainty and expert knowledge. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.03.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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