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Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med 2023; 4:101213. [PMID: 37788667 PMCID: PMC10591058 DOI: 10.1016/j.xcrm.2023.101213] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/07/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
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Affiliation(s)
- Zhouyu Guan
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Ruhan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Furong Laboratory, Changsha, Hunan 41000, China
| | - Chun Cai
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yuexing Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Jiajia Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Shan Huang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Wu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Dan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Shujie Yu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Zheyuan Wang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Shu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xuhong Hou
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiping Jia
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
| | - Bin Sheng
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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Tavakolian A, Hajati F, Rezaee A, Fasakhodi AO, Uddin S. Fast COVID-19 versus H1N1 screening using Optimized Parallel Inception. EXPERT SYSTEMS WITH APPLICATIONS 2022; 204:117551. [PMID: 35611121 PMCID: PMC9119711 DOI: 10.1016/j.eswa.2022.117551] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 05/03/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
COVID-19 and swine-origin influenza A (H1N1) are both pandemics that sparked significant concern worldwide. Since these two diseases have common symptoms, a fast COVID-19 versus H1N1 screening helps better manage patients at healthcare facilities. We present a novel deep model, called Optimized Parallel Inception, for fast screening of COVID-19 and H1N1 patients. We also present a Semi-supervised Generative Adversarial Network (SGAN) to address the problem related to the smaller size of the COVID-19 and H1N1 research data. To evaluate the proposed models, we have merged two separate COVID-19 and H1N1 data from different sources to build a new dataset. The created dataset includes 4,383 positive COVID-19 cases, 989 positive H1N1 cases, and 1,059 negative cases. We applied SGAN on this dataset to remove issues related to unequal class densities. The experimental results show that the proposed model's screening accuracy is 99.2% and 99.6% for COVID-19 and H1N1, respectively. According to our analysis, the most significant symptoms and underlying chronic diseases for COVID-19 versus H1N1 screening are dry cough, breathing problems, diabetes, and gastrointestinal.
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Affiliation(s)
- Alireza Tavakolian
- Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran 1439957131, Iran
| | - Farshid Hajati
- College of Engineering and Science, Victoria University Sydney, Sydney, NSW 2000, Australia
| | - Alireza Rezaee
- Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran 1439957131, Iran
| | - Amirhossein Oliaei Fasakhodi
- Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran 1439957131, Iran
| | - Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia
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Xie F, Yuan H, Ning Y, Ong MEH, Feng M, Hsu W, Chakraborty B, Liu N. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. J Biomed Inform 2021; 126:103980. [PMID: 34974189 DOI: 10.1016/j.jbi.2021.103980] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/07/2021] [Accepted: 12/20/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Temporal electronic health records (EHRs) contain a wealth of information for secondary uses, such as clinical events prediction and chronic disease management. However, challenges exist for temporal data representation. We therefore sought to identify these challenges and evaluate novel methodologies for addressing them through a systematic examination of deep learning solutions. METHODS We searched five databases (PubMed, Embase, the Institute of Electrical and Electronics Engineers [IEEE] Xplore Digital Library, the Association for Computing Machinery [ACM] Digital Library, and Web of Science) complemented with hand-searching in several prestigious computer science conference proceedings. We sought articles that reported deep learning methodologies on temporal data representation in structured EHR data from January 1, 2010, to August 30, 2020. We summarized and analyzed the selected articles from three perspectives: nature of time series, methodology, and model implementation. RESULTS We included 98 articles related to temporal data representation using deep learning. Four major challenges were identified, including data irregularity, heterogeneity, sparsity, and model opacity. We then studied how deep learning techniques were applied to address these challenges. Finally, we discuss some open challenges arising from deep learning. CONCLUSION Temporal EHR data present several major challenges for clinical prediction modeling and data utilization. To some extent, current deep learning solutions can address these challenges. Future studies may consider designing comprehensive and integrated solutions. Moreover, researchers should incorporate clinical domain knowledge into study designs and enhance model interpretability to facilitate clinical implementation.
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Affiliation(s)
- Feng Xie
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Han Yuan
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Yilin Ning
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Marcus Eng Hock Ong
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Wynne Hsu
- School of Computing, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore
| | - Bibhas Chakraborty
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; Department of Statistics and Data Science, National University of Singapore, Singapore; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; Institute of Data Science, National University of Singapore, Singapore; SingHealth AI Health Program, Singapore Health Services, Singapore.
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Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: A review. Metabolism 2021; 124:154872. [PMID: 34480920 DOI: 10.1016/j.metabol.2021.154872] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/27/2021] [Accepted: 08/28/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.
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Affiliation(s)
- Thibault Gautier
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America.
| | - Leah B Ziegler
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Matthew S Gerber
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Enrique Campos-Náñez
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Stephen D Patek
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
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Tozzo V, Azencott CA, Fiorini S, Fava E, Trucco A, Barla A. Where Do We Stand in Regularization for Life Science Studies? J Comput Biol 2021; 29:213-232. [PMID: 33926217 PMCID: PMC8968832 DOI: 10.1089/cmb.2019.0371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
More and more biologists and bioinformaticians turn to machine learning to analyze large amounts of data. In this context, it is crucial to understand which is the most suitable data analysis pipeline for achieving reliable results. This process may be challenging, due to a variety of factors, the most crucial ones being the data type and the general goal of the analysis (e.g., explorative or predictive). Life science data sets require further consideration as they often contain measures with a low signal-to-noise ratio, high-dimensional observations, and relatively few samples. In this complex setting, regularization, which can be defined as the introduction of additional information to solve an ill-posed problem, is the tool of choice to obtain robust models. Different regularization practices may be used depending both on characteristics of the data and of the question asked, and different choices may lead to different results. In this article, we provide a comprehensive description of the impact and importance of regularization techniques in life science studies. In particular, we provide an intuition of what regularization is and of the different ways it can be implemented and exploited. We propose four general life sciences problems in which regularization is fundamental and should be exploited for robustness. For each of these large families of problems, we enumerate different techniques as well as examples and case studies. Lastly, we provide a unified view of how to approach each data type with various regularization techniques.
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Affiliation(s)
- Veronica Tozzo
- Department of Informatics, Bioengineering, Robotics and System Engineering-DIBRIS, University of Genoa, Genoa, Italy
| | - Chloé-Agathe Azencott
- Centre for Computational Biology-CBIO, MINES ParisTech, PSL Research University, Paris, France.,Institut Curie, PSL Research University, Paris, France.,INSERM, U900, Paris, France
| | | | - Emanuele Fava
- Departiment of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Genoa, Italy
| | - Andrea Trucco
- Departiment of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Genoa, Italy
| | - Annalisa Barla
- Department of Informatics, Bioengineering, Robotics and System Engineering-DIBRIS, University of Genoa, Genoa, Italy
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