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Van R, Alvarez D, Mize T, Gannavarapu S, Chintham Reddy L, Nasoz F, Han MV. A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies. BMC Bioinformatics 2024; 25:181. [PMID: 38720247 PMCID: PMC11080237 DOI: 10.1186/s12859-024-05801-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins. RESULTS We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer. CONCLUSION By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.
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Affiliation(s)
- Richard Van
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Daniel Alvarez
- Department of Computer Science, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Travis Mize
- Icahn School of Medicine at Mount Sinai, Institute for Genomic Health, New York City, NY, USA
| | - Sravani Gannavarapu
- Department of Computer Science, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Lohitha Chintham Reddy
- Department of Computer Science, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Fatma Nasoz
- Department of Computer Science, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Mira V Han
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA.
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA.
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Agnihotri A, Ramasubbu SK, Bandyopadhyay A, Bidarolli M, Nath UK, Das B. Prevalence, Attributes, and Risk Factors of QT-Interval-Prolonging Drugs and Potential Drug-Drug Interactions in Cancer Patients: A Prospective Study in a Tertiary Care Hospital. Cureus 2024; 16:e60492. [PMID: 38882995 PMCID: PMC11180424 DOI: 10.7759/cureus.60492] [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] [Accepted: 05/17/2024] [Indexed: 06/18/2024] Open
Abstract
Introduction Cancer chemotherapy regimens include multiple classes of adjuvant drugs as supportive therapy. Because of the concurrent intake of other drugs (like antiemetics, antidepressants, analgesics, and antimicrobials), there is a heightened risk for possible QT interval prolongation. There is a dearth of evidence in the literature regarding the usage of QT-prolonging anticancer drugs and associated risk factors that have the propensity to prolong QT interval. The purpose was to explore the extent of the use of QT-interval-prolonging drugs and potential QT-prolonging drug-drug interactions (QT-DDIs) in cancer patients attending OPD in a tertiary-care hospital. Methods This was a hospital-based, cross-sectional, observational study. Risk stratification of QT-prolonging drugs for torsades de pointes (TdP) was done by the Arizona Center for Education and Research on Therapeutics (AzCERT)/CredibleMeds-lists, and potential QT-DDIs were determined with four online DDI-checker-software. Results In 1331 cancer patients, the overall prevalence of potential QT-prolonging drug utilization was 97.3%. Ondansetron, pantoprazole, domperidone, and olanzapine were the most frequent QT-prolonging drugs in cancer patients. The top six antineoplastics with potential QT-prolonging and torsadogenic actions were capecitabine, oxaliplatin, imatinib, bortezomib, 5-fluorouracil, and bendamustine. Evidence-based pragmatic QTc interval prolongation risk assessment tools are imperative for cancer patients. Conclusion This study revealed a high prevalence of QT-prolonging drugs and QT-DDIs among cancer patients who are treated with anticancer and non-anticancer drugs. As a result, it's critical to take precautions, stay vigilant, and avoid QT-prolonging in clinical situations. Evidence-based pragmatic QTc interval prolongation risk assessment tools are needed for cancer patients.
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Affiliation(s)
- Akash Agnihotri
- Department of Pharmacology, Amrita School of Medicine, Faridabad, IND
| | - Saravana Kumar Ramasubbu
- Department of Pharmacology, Andaman and Nicobar Islands Institute of Medical Sciences, Port Blair, IND
| | - Arkapal Bandyopadhyay
- Department of Pharmacology, All India Institute of Medical Sciences, Kalyani, Kalyani, IND
| | - Manjunath Bidarolli
- Department of Pharmacology, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Uttam Kumar Nath
- Department of Medical Oncology and Hematology, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Biswadeep Das
- Department of Pharmacology, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
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Xu Y, Cao L, Chen Y, Zhang Z, Liu W, Li H, Ding C, Pu J, Qian K, Xu W. Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation. SMALL METHODS 2024:e2400305. [PMID: 38682615 DOI: 10.1002/smtd.202400305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 04/07/2024] [Indexed: 05/01/2024]
Abstract
Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.
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Affiliation(s)
- Yudian Xu
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Linlin Cao
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Yifan Chen
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Ziyue Zhang
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Wanshan Liu
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - He Li
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Chenhuan Ding
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Jun Pu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Wei Xu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
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Choi Y, Lee J, Shin K, Lee JW, Kim JW, Lee S, Choi YJ, Park KH, Kim JH. Integrated clinical and genomic models using machine-learning methods to predict the efficacy of paclitaxel-based chemotherapy in patients with advanced gastric cancer. BMC Cancer 2024; 24:502. [PMID: 38643078 PMCID: PMC11031899 DOI: 10.1186/s12885-024-12268-9] [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: 01/19/2024] [Accepted: 04/16/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND Paclitaxel is commonly used as a second-line therapy for advanced gastric cancer (AGC). The decision to proceed with second-line chemotherapy and select an appropriate regimen is critical for vulnerable patients with AGC progressing after first-line chemotherapy. However, no predictive biomarkers exist to identify patients with AGC who would benefit from paclitaxel-based chemotherapy. METHODS This study included 288 patients with AGC receiving second-line paclitaxel-based chemotherapy between 2017 and 2022 as part of the K-MASTER project, a nationwide government-funded precision medicine initiative. The data included clinical (age [young-onset vs. others], sex, histology [intestinal vs. diffuse type], prior trastuzumab use, duration of first-line chemotherapy), and genomic factors (pathogenic or likely pathogenic variants). Data were randomly divided into training and validation sets (0.8:0.2). Four machine learning (ML) methods, namely random forest (RF), logistic regression (LR), artificial neural network (ANN), and ANN with genetic embedding (ANN with GE), were used to develop the prediction model and validated in the validation sets. RESULTS The median patient age was 64 years (range 25-91), and 65.6% of those were male. A total of 288 patients were divided into the training (n = 230) and validation (n = 58) sets. No significant differences existed in baseline characteristics between the training and validation sets. In the training set, the areas under the ROC curves (AUROC) for predicting better progression-free survival (PFS) with paclitaxel-based chemotherapy were 0.499, 0.679, 0.618, and 0.732 in the RF, LR, ANN, and ANN with GE models, respectively. The ANN with the GE model that achieved the highest AUROC recorded accuracy, sensitivity, specificity, and F1-score performance of 0.458, 0.912, 0.724, and 0.579, respectively. In the validation set, the ANN with GE model predicted that paclitaxel-sensitive patients had significantly longer PFS (median PFS 7.59 vs. 2.07 months, P = 0.020) and overall survival (OS) (median OS 14.70 vs. 7.50 months, P = 0.008). The LR model predicted that paclitaxel-sensitive patients showed a trend for longer PFS (median PFS 6.48 vs. 2.33 months, P = 0.078) and OS (median OS 12.20 vs. 8.61 months, P = 0.099). CONCLUSIONS These ML models, integrated with clinical and genomic factors, offer the possibility to help identify patients with AGC who may benefit from paclitaxel chemotherapy.
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Grants
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- HR22C1302 Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
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Affiliation(s)
- Yonghwa Choi
- Department of Computer Science and Engineering, Korea University, Seoul, Korea
- OncoMASTER Inc., Seoul, Korea
| | - Jangwoo Lee
- Institute of Human Behavior & Genetic, Korea University College of Medicine, Seoul, Korea
- Biomedical Research Center, Korea University Anam Hospital, Seoul, Korea
| | - Keewon Shin
- Biomedical Research Center, Korea University Anam Hospital, Seoul, Korea
| | - Ji Won Lee
- Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Ju Won Kim
- Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Soohyeon Lee
- Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Yoon Ji Choi
- Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Kyong Hwa Park
- Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jwa Hoon Kim
- Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
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Kuwatsuka Y, Kasajima R, Yamaguchi R, Uchida N, Konuma T, Tanaka M, Shingai N, Miyakoshi S, Kozai Y, Uehara Y, Eto T, Toyosaki M, Nishida T, Ishimaru F, Kato K, Fukuda T, Imoto S, Atsuta Y, Takahashi S. Machine Learning Prediction Model for Neutrophil Recovery after Unrelated Cord Blood Transplantation. Transplant Cell Ther 2024; 30:444.e1-444.e11. [PMID: 38336299 DOI: 10.1016/j.jtct.2024.02.001] [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: 11/25/2023] [Revised: 01/27/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
Delayed neutrophil recovery is an important limitation to the administration of cord blood transplantation (CBT) and leaves the recipient vulnerable to life-threatening infection and increases the risk of other complications. A predictive model for neutrophil recovery after single-unit CBT was developed by using a machine learning method, which can handle large and complex datasets, allowing for the analysis of massive amounts of information to uncover patterns and make accurate predictions. Japanese registry data, the largest real-world dataset of CBT, was selected as the data source. Ninety-eight variables with observed values for >80% of the subjects known at the time of CBT were selected. Model building was performed with a competing risk regression model with lasso penalty. Prediction accuracy of the models was evaluated by calculating the area under the receiver operating characteristic curve (AUC) using a test dataset. The primary outcome was neutrophil recovery at day (D) 28, with recovery at D14 and D42 analyzed as secondary outcomes. The final cord blood engraftment prediction (CBEP) models included 2991 single-unit CBT recipients with acute leukemia. The median AUC of a D28-CBEP lasso regression model run 100 times was .74, and those for D14 and D42 were .88 and .68, respectively. The predictivity of the D28-CBEP model was higher than that of 4 different legacy models constructed separately. A highly predictive model for neutrophil recovery by 28 days after CBT was constructed using machine learning techniques; however, identification of significant risk factors was insufficient for outcome prediction for an individual patient, which is necessary for improving therapeutic outcomes. Notably, the prediction accuracy for post-transplantation D14, D28, and D42 decreased, and the model became more complex with more associated factors with increased time after transplantation.
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Affiliation(s)
- Yachiyo Kuwatsuka
- Department of Advanced Medicine, Nagoya University Hospital, Nagoya, Japan
| | - Rika Kasajima
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Japan
| | - Rui Yamaguchi
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Japan; Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Naoyuki Uchida
- Department of Hematology, Federation of National Public Service Personnel Mutual Aid Associations Toranomon Hospital, Tokyo, Japan
| | - Takaaki Konuma
- Department of Hematology/Oncology, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Masatsugu Tanaka
- Department of Hematology, Kanagawa Cancer Center, Yokohama, Japan
| | - Naoki Shingai
- Hematology Division, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, Tokyo, Japan
| | - Shigesaburo Miyakoshi
- Department of Hematology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Yasuji Kozai
- Department of Hematology, Tokyo Metropolitan Tama Medical Center, Fuchu, Japan
| | - Yasufumi Uehara
- Department of Hematology, Kitakyushu City Hospital Organization, Kitakyushu Municipal Medical Center, Kitakyushu, Japan
| | - Tetsuya Eto
- Department of Hematology, Hamanomachi Hospital, Fukuoka, Japan
| | - Masako Toyosaki
- Department of Hematology/Oncology, Tokai University School of Medicine, Isehara, Japan
| | - Tetsuya Nishida
- Department of Hematology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Japan
| | - Fumihiko Ishimaru
- Japanese Red Cross Kanto-Koshinetsu Block Blood Center, Atsugi, Japan
| | - Koji Kato
- Central Japan Cord Blood Bank, Seto, Japan
| | - Takahiro Fukuda
- Department of Hematopoietic Stem Cell Transplantation, National Cancer Center Hospital, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yoshiko Atsuta
- Japanese Data Center for Hematopoietic Cell Transplantation, Nagakute, Japan; Department of Registry Science for Transplant and Cellular Therapy, Aichi Medical University School of Medicine, Nagakute, Japan.
| | - Satoshi Takahashi
- Department of Hematology/Oncology, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
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Chowdhury MA, Zhang JJ, Rizk R, Chen WCW. Stem cell therapy for heart failure in the clinics: new perspectives in the era of precision medicine and artificial intelligence. Front Physiol 2024; 14:1344885. [PMID: 38264333 PMCID: PMC10803627 DOI: 10.3389/fphys.2023.1344885] [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: 11/26/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Stem/progenitor cells have been widely evaluated as a promising therapeutic option for heart failure (HF). Numerous clinical trials with stem/progenitor cell-based therapy (SCT) for HF have demonstrated encouraging results, but not without limitations or discrepancies. Recent technological advancements in multiomics, bioinformatics, precision medicine, artificial intelligence (AI), and machine learning (ML) provide new approaches and insights for stem cell research and therapeutic development. Integration of these new technologies into stem/progenitor cell therapy for HF may help address: 1) the technical challenges to obtain reliable and high-quality therapeutic precursor cells, 2) the discrepancies between preclinical and clinical studies, and 3) the personalized selection of optimal therapeutic cell types/populations for individual patients in the context of precision medicine. This review summarizes the current status of SCT for HF in clinics and provides new perspectives on the development of computation-aided SCT in the era of precision medicine and AI/ML.
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Affiliation(s)
- Mohammed A. Chowdhury
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, United States
- Department of Public Health and Health Sciences, Health Sciences Ph.D. Program, School of Health Sciences, University of South Dakota, Vermillion, SD, United States
- Department of Cardiology, North Central Heart, Avera Heart Hospital, Sioux Falls, SD, United States
| | - Jing J. Zhang
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, United States
| | - Rodrigue Rizk
- Department of Computer Science, University of South Dakota, Vermillion, SD, United States
| | - William C. W. Chen
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, United States
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Guo C, Wu JY. Pathogen Discovery in the Post-COVID Era. Pathogens 2024; 13:51. [PMID: 38251358 PMCID: PMC10821006 DOI: 10.3390/pathogens13010051] [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: 11/11/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
Pathogen discovery plays a crucial role in the fields of infectious diseases, clinical microbiology, and public health. During the past four years, the global response to the COVID-19 pandemic highlighted the importance of early and accurate identification of novel pathogens for effective management and prevention of outbreaks. The post-COVID era has ushered in a new phase of infectious disease research, marked by accelerated advancements in pathogen discovery. This review encapsulates the recent innovations and paradigm shifts that have reshaped the landscape of pathogen discovery in response to the COVID-19 pandemic. Primarily, we summarize the latest technology innovations, applications, and causation proving strategies that enable rapid and accurate pathogen discovery for both acute and historical infections. We also explored the significance and the latest trends and approaches being employed for effective implementation of pathogen discovery from various clinical and environmental samples. Furthermore, we emphasize the collaborative nature of the pandemic response, which has led to the establishment of global networks for pathogen discovery.
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Affiliation(s)
- Cheng Guo
- Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jian-Yong Wu
- School of Public Health, Xinjiang Medical University, Urumqi 830017, China
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Salimi M, Roshanfar M, Tabatabaei N, Mosadegh B. Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine. J Pers Med 2023; 14:33. [PMID: 38248734 PMCID: PMC10817559 DOI: 10.3390/jpm14010033] [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: 10/24/2023] [Revised: 12/08/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.
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Affiliation(s)
| | - Majid Roshanfar
- Department of Mechanical Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;
| | - Nima Tabatabaei
- Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada;
| | - Bobak Mosadegh
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA
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Grieb N, Schmierer L, Kim HU, Strobel S, Schulz C, Meschke T, Kubasch AS, Brioli A, Platzbecker U, Neumuth T, Merz M, Oeser A. A digital twin model for evidence-based clinical decision support in multiple myeloma treatment. Front Digit Health 2023; 5:1324453. [PMID: 38173909 PMCID: PMC10761485 DOI: 10.3389/fdgth.2023.1324453] [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: 10/19/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.
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Affiliation(s)
- Nora Grieb
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Lukas Schmierer
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Hyeon Ung Kim
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Sarah Strobel
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Christian Schulz
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Tim Meschke
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Anne Sophie Kubasch
- Department of Hematology, Hemostaseology, Cellular Therapy and Infectiology, University Hospital of Leipzig, Leipzig, Germany
| | - Annamaria Brioli
- Clinic of Internal Medicine C, Hematology and Oncology, Stem Cell Transplantation and Palliative Care, Greifswald University Medicine, Greifswald, Germany
| | - Uwe Platzbecker
- Department of Hematology, Hemostaseology, Cellular Therapy and Infectiology, University Hospital of Leipzig, Leipzig, Germany
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Maximilian Merz
- Department of Hematology, Hemostaseology, Cellular Therapy and Infectiology, University Hospital of Leipzig, Leipzig, Germany
| | - Alexander Oeser
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
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10
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Shi J, Bendig D, Vollmar HC, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. J Med Internet Res 2023; 25:e45815. [PMID: 38064255 PMCID: PMC10746970 DOI: 10.2196/45815] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 08/16/2023] [Accepted: 09/30/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. OBJECTIVE In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. METHODS Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. RESULTS From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. CONCLUSIONS The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.
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Affiliation(s)
- Jin Shi
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | - David Bendig
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | | | - Peter Rasche
- Department of Healthcare, University of Applied Science - Hochschule Niederrhein, Krefeld, Germany
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11
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Caiafa CF, Sun Z, Tanaka T, Marti-Puig P, Solé-Casals J. Special Issue "Machine Learning Methods for Biomedical Data Analysis". SENSORS (BASEL, SWITZERLAND) 2023; 23:9377. [PMID: 38067750 PMCID: PMC10708713 DOI: 10.3390/s23239377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 12/18/2023]
Abstract
Machine learning is an effective method for developing automatic algorithms for analysing sophisticated biomedical data [...].
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Affiliation(s)
- Cesar F. Caiafa
- Instituto Argentino de Radioastronomía—CCT La Plata, CONICET/CIC-PBA/UNLP, V. Elisa 1894, Argentina
| | - Zhe Sun
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako-Shi 351-0198, Japan;
| | - Toshihisa Tanaka
- Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan;
| | - Pere Marti-Puig
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, 08500 Vic, Catalonia, Spain;
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, 08500 Vic, Catalonia, Spain;
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12
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Abdallah N, Marion JM, Tauber C, Carlier T, Hatt M, Chauvet P. Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques. Sci Rep 2023; 13:20014. [PMID: 37973797 PMCID: PMC10654662 DOI: 10.1038/s41598-023-46239-0] [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: 04/20/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023] Open
Abstract
This study aims to develop a robust pipeline for classifying invasive ductal carcinomas and benign tumors in histopathological images, addressing variability within and between centers. We specifically tackle the challenge of detecting atypical data and variability between common clusters within the same database. Our feature engineering-based pipeline comprises a feature extraction step, followed by multiple harmonization techniques to rectify intra- and inter-center batch effects resulting from image acquisition variability and diverse patient clinical characteristics. These harmonization steps facilitate the construction of more robust and efficient models. We assess the proposed pipeline's performance on two public breast cancer databases, BreaKHIS and IDCDB, utilizing recall, precision, and accuracy metrics. Our pipeline outperforms recent models, achieving 90-95% accuracy in classifying benign and malignant tumors. We demonstrate the advantage of harmonization for classifying patches from different databases. Our top model scored 94.7% for IDCDB and 95.2% for BreaKHis, surpassing existing feature engineering-based models (92.1% for IDCDB and 87.7% for BreaKHIS) and attaining comparable performance to deep learning models. The proposed feature-engineering-based pipeline effectively classifies malignant and benign tumors while addressing variability within and between centers through the incorporation of various harmonization techniques. Our findings reveal that harmonizing variabilities between patches from different batches directly impacts the learning and testing performance of classification models. This pipeline has the potential to enhance breast cancer diagnosis and treatment and may be applicable to other diseases.
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Affiliation(s)
- Nassib Abdallah
- LaTIM, INSERM, Université de Bretagne-Occidentale, Brest, France.
- LARIS, Université d'Angers, Angers, France.
| | | | | | | | - Mathieu Hatt
- LaTIM, INSERM, Université de Bretagne-Occidentale, Brest, France
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13
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Vicente L, Matute H. Humans inherit artificial intelligence biases. Sci Rep 2023; 13:15737. [PMID: 37789032 PMCID: PMC10547752 DOI: 10.1038/s41598-023-42384-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/09/2023] [Indexed: 10/05/2023] Open
Abstract
Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypothesized that people who perform a (simulated) medical diagnostic task assisted by a biased AI system will reproduce the model's bias in their own decisions, even when they move to a context without AI support. In three experiments, participants completed a medical-themed classification task with or without the help of a biased AI system. The biased recommendations by the AI influenced participants' decisions. Moreover, when those participants, assisted by the AI, moved on to perform the task without assistance, they made the same errors as the AI had made during the previous phase. Thus, participants' responses mimicked AI bias even when the AI was no longer making suggestions. These results provide evidence of human inheritance of AI bias.
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Affiliation(s)
- Lucía Vicente
- Department of Psychology, Deusto University, Avenida Universidades 24, 48007, Bilbao, Spain
| | - Helena Matute
- Department of Psychology, Deusto University, Avenida Universidades 24, 48007, Bilbao, Spain.
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14
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Wang SM, Hogg HDJ, Sangvai D, Patel MR, Weissler EH, Kellogg KC, Ratliff W, Balu S, Sendak M. Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study. JMIR Form Res 2023; 7:e43963. [PMID: 37733427 PMCID: PMC10557008 DOI: 10.2196/43963] [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: 11/02/2022] [Revised: 01/20/2023] [Accepted: 04/30/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Machine learning (ML)-driven clinical decision support (CDS) continues to draw wide interest and investment as a means of improving care quality and value, despite mixed real-world implementation outcomes. OBJECTIVE This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely guideline-based care. METHODS A total of 12 semistructured interviews were conducted with individuals from 3 stakeholder groups during the first 4 weeks of integration of an ML-driven CDS. The stakeholder groups included technical, administrative, and clinical members of the team interacting with the ML-driven CDS. The ML-driven CDS identified patients with a high probability of having PAD, and these patients were then reviewed by an interdisciplinary team that developed a recommended action plan and sent recommendations to the patient's primary care provider. Pseudonymized transcripts were coded, and thematic analysis was conducted by a multidisciplinary research team. RESULTS Three themes were identified: positive factors translating in silico performance to real-world efficacy, organizational factors and data structure factors affecting clinical impact, and potential challenges to advancing equity. Our study found that the factors that led to successful translation of in silico algorithm performance to real-world impact were largely nontechnical, given adequate efficacy in retrospective validation, including strong clinical leadership, trustworthy workflows, early consideration of end-user needs, and ensuring that the CDS addresses an actionable problem. Negative factors of integration included failure to incorporate the on-the-ground context, the lack of feedback loops, and data silos limiting the ML-driven CDS. The success criteria for each stakeholder group were also characterized to better understand how teams work together to integrate ML-driven CDS and to understand the varying needs across stakeholder groups. CONCLUSIONS Longitudinal and multidisciplinary stakeholder engagement in the development and integration of ML-driven CDS underpins its effective translation into real-world care. Although previous studies have focused on the technical elements of ML-driven CDS, our study demonstrates the importance of including administrative and operational leaders as well as an early consideration of clinicians' needs. Seeing how different stakeholder groups have this more holistic perspective also permits more effective detection of context-driven health care inequities, which are uncovered or exacerbated via ML-driven CDS integration through structural and organizational challenges. Many of the solutions to these inequities lie outside the scope of ML and require coordinated systematic solutions for mitigation to help reduce disparities in the care of patients with PAD.
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Affiliation(s)
- Sabrina M Wang
- Duke University School of Medicine, Durham, NC, United States
| | - H D Jeffry Hogg
- Population Health Science Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle Eye Centre, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Devdutta Sangvai
- Population Health Management, Duke Health, Durham, NC, United States
| | - Manesh R Patel
- Department of Cardiology, Duke University, Durham, NC, United States
| | - E Hope Weissler
- Department of Vascular Surgery, Duke University, Durham, NC, United States
| | | | - William Ratliff
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Mark Sendak
- Duke Institute for Health Innovation, Durham, NC, United States
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15
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Ageno A, Català N, Pons M. Acquisition of temporal patterns from electronic health records: an application to multimorbid patients. BMC Med Inform Decis Mak 2023; 23:189. [PMID: 37726756 PMCID: PMC10510308 DOI: 10.1186/s12911-023-02287-0] [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: 05/08/2023] [Accepted: 09/04/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish. METHODS We propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances. RESULTS As we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients. CONCLUSION Our algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool.
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Affiliation(s)
- Alicia Ageno
- TALP Research Center, Computer Science Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
- IDEAI-UPC Research Center, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
| | - Neus Català
- TALP Research Center, Computer Science Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- IDEAI-UPC Research Center, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Marcel Pons
- Facultat d'Informàtica de Barcelona, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
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16
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Ayton SG, Treviño V. MuTATE-an R package for comprehensive multi-objective molecular modeling. Bioinformatics 2023; 39:btad507. [PMID: 37688581 PMCID: PMC10500092 DOI: 10.1093/bioinformatics/btad507] [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: 05/11/2023] [Revised: 07/14/2023] [Accepted: 09/07/2023] [Indexed: 09/11/2023] Open
Abstract
MOTIVATION Comprehensive multi-omics studies have driven advances in disease modeling for effective precision medicine but pose a challenge for existing machine-learning approaches, which have limited interpretability across clinical endpoints. Automated, comprehensive disease modeling requires a machine-learning approach that can simultaneously identify disease subgroups and their defining molecular biomarkers by explaining multiple clinical endpoints. Current tools are restricted to individual endpoints or limited variable types, necessitate advanced computation skills, and require resource-intensive manual expert interpretation. RESULTS We developed Multi-Target Automated Tree Engine (MuTATE) for automated and comprehensive molecular modeling, which enables user-friendly multi-objective decision tree construction and visualization of relationships between molecular biomarkers and patient subgroups characterized by multiple clinical endpoints. MuTATE incorporates multiple targets throughout model construction and allows for target weights, enabling construction of interpretable decision trees that provide insights into disease heterogeneity and molecular signatures. MuTATE eliminates the need for manual synthesis of multiple non-explainable models, making it highly efficient and accessible for bioinformaticians and clinicians. The flexibility and versatility of MuTATE make it applicable to a wide range of complex diseases, including cancer, where it can improve therapeutic decisions by providing comprehensive molecular insights for precision medicine. MuTATE has the potential to transform biomarker discovery and subtype identification, leading to more effective and personalized treatment strategies in precision medicine, and advancing our understanding of disease mechanisms at the molecular level. AVAILABILITY AND IMPLEMENTATION MuTATE is freely available at GitHub (https://github.com/SarahAyton/MuTATE) under the GPLv3 license.
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Affiliation(s)
- Sarah G Ayton
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico
| | - Víctor Treviño
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico
- Tecnologico de Monterrey, The Institute for Obesity Research, Monterrey, Mexico
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17
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Cohen Y, Valdés-Mas R, Elinav E. The Role of Artificial Intelligence in Deciphering Diet-Disease Relationships: Case Studies. Annu Rev Nutr 2023; 43:225-250. [PMID: 37207358 DOI: 10.1146/annurev-nutr-061121-090535] [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] [Indexed: 05/21/2023]
Abstract
Modernization of society from a rural, hunter-gatherer setting into an urban and industrial habitat, with the associated dietary changes, has led to an increased prevalence of cardiometabolic and additional noncommunicable diseases, such as cancer, inflammatory bowel disease, and neurodegenerative and autoimmune disorders. However, while dietary sciences have been rapidly evolving to meet these challenges, validation and translation of experimental results into clinical practice remain limited for multiple reasons, including inherent ethnic, gender, and cultural interindividual variability, among other methodological, dietary reporting-related, and analytical issues. Recently, large clinical cohorts with artificial intelligence analytics have introduced new precision and personalized nutrition concepts that enable one to successfully bridge these gaps in a real-life setting. In this review, we highlight selected examples of case studies at the intersection between diet-disease research and artificial intelligence. We discuss their potential and challenges and offer an outlook toward the transformation of dietary sciences into individualized clinical translation.
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Affiliation(s)
- Yotam Cohen
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel;
| | - Rafael Valdés-Mas
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel;
| | - Eran Elinav
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel;
- Division of Microbiome & Cancer, National German Cancer Research Center (DKFZ), Heidelberg, Germany;
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18
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Gu B, Sidhu S, Weinreb RN, Christopher M, Zangwill LM, Baxter SL. Review of Visualization Approaches in Deep Learning Models of Glaucoma. Asia Pac J Ophthalmol (Phila) 2023; 12:392-401. [PMID: 37523431 DOI: 10.1097/apo.0000000000000619] [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: 03/16/2023] [Accepted: 05/11/2023] [Indexed: 08/02/2023] Open
Abstract
Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are important in delaying progression. Deep learning (DL) has emerged as a rapidly advancing tool to help achieve these objectives. In this narrative review, data types and visualization approaches for presenting model predictions, including models based on tabular data, functional data, and/or structural data, are summarized, and the importance of data source diversity for improving the utility and generalizability of DL models is explored. Examples of innovative approaches to understanding predictions of artificial intelligence (AI) models and alignment with clinicians are provided. In addition, methods to enhance the interpretability of clinical features from tabular data used to train AI models are investigated. Examples of published DL models that include interfaces to facilitate end-user engagement and minimize cognitive and time burdens are highlighted. The stages of integrating AI models into existing clinical workflows are reviewed, and challenges are discussed. Reviewing these approaches may help inform the generation of user-friendly interfaces that are successfully integrated into clinical information systems. This review details key principles regarding visualization approaches in DL models of glaucoma. The articles reviewed here focused on usability, explainability, and promotion of clinician trust to encourage wider adoption for clinical use. These studies demonstrate important progress in addressing visualization and explainability issues required for successful real-world implementation of DL models in glaucoma.
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Affiliation(s)
- Byoungyoung Gu
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
| | - Sophia Sidhu
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
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19
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Forrest IS, Petrazzini BO, Duffy Á, Park JK, O'Neal AJ, Jordan DM, Rocheleau G, Nadkarni GN, Cho JH, Blazer AD, Do R. A machine learning model identifies patients in need of autoimmune disease testing using electronic health records. Nat Commun 2023; 14:2385. [PMID: 37169741 PMCID: PMC10130143 DOI: 10.1038/s41467-023-37996-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 04/05/2023] [Indexed: 05/13/2023] Open
Abstract
Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.
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Affiliation(s)
- Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben O Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anya J O'Neal
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashira D Blazer
- Division of Rheumatology, Hospital for Special Surgery, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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20
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Wang B, Li Y, Zhou M, Han Y, Zhang M, Gao Z, Liu Z, Chen P, Du W, Zhang X, Feng X, Liu BF. Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence. Nat Commun 2023; 14:1341. [PMID: 36906581 PMCID: PMC10007670 DOI: 10.1038/s41467-023-36017-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 01/10/2023] [Indexed: 03/13/2023] Open
Abstract
The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.
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Affiliation(s)
- Bangfeng Wang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yiwei Li
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Mengfan Zhou
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yulong Han
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Mingyu Zhang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zhaolong Gao
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zetai Liu
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Peng Chen
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wei Du
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xingcai Zhang
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
| | - Xiaojun Feng
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Bi-Feng Liu
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
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21
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Luo J, Lan L, Huang S, Zeng X, Xiang Q, Li M, Yang S, Zhao W, Zhou X. Real-time prediction of organ failures in patients with acute pancreatitis using longitudinal irregular data. J Biomed Inform 2023; 139:104310. [PMID: 36773821 DOI: 10.1016/j.jbi.2023.104310] [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: 04/21/2022] [Revised: 01/10/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023]
Abstract
It is extremely important to identify patients with acute pancreatitis who are at high risk for developing persistent organ failures early in the course of the disease. Due to the irregularity of longitudinal data and the poor interpretability of complex models, many models used to identify acute pancreatitis patients with a high risk of organ failure tended to rely on simple statistical models and limited their application to the early stages of patient admission. With the success of recurrent neural networks in modeling longitudinal medical data and the development of interpretable algorithms, these problems can be well addressed. In this study, we developed a novel model named Multi-task and Time-aware Gated Recurrent Unit RNN (MT-GRU) to directly predict organ failure in patients with acute pancreatitis based on irregular medical EMR data. Our proposed end-to-end multi-task model achieved significantly better performance compared to two-stage models. In addition, our model not only provided an accurate early warning of organ failure for patients throughout their hospital stay, but also demonstrated individual and population-level important variables, allowing physicians to understand the scientific basis of the model for decision-making. By providing early warning of the risk of organ failure, our proposed model is expected to assist physicians in improving outcomes for patients with acute pancreatitis.
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Affiliation(s)
- Jiawei Luo
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China.
| | - Lan Lan
- IT Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Shixin Huang
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.
| | - Xiaoxi Zeng
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China.
| | - Qu Xiang
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China.
| | - Mengjiao Li
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China.
| | - Shu Yang
- College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
| | - Weiling Zhao
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA.
| | - Xiaobo Zhou
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA.
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22
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Chiu CC, Wu CM, Chien TN, Kao LJ, Li C, Chu CM. Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4340. [PMID: 36901354 PMCID: PMC10001457 DOI: 10.3390/ijerph20054340] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient's age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.
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Affiliation(s)
- Chih-Chou Chiu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chung-Min Wu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Te-Nien Chien
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Ling-Jing Kao
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chengcheng Li
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chuan-Mei Chu
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
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23
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Taribagil P, Hogg HDJ, Balaskas K, Keane PA. Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities. EXPERT REVIEW OF OPHTHALMOLOGY 2023. [DOI: 10.1080/17469899.2023.2175672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Priyal Taribagil
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - HD Jeffry Hogg
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Department of Population Health Science, Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Ophthalmology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Freeman Road, Newcastle upon Tyne, UK
| | - Konstantinos Balaskas
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
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24
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Reeder S, Foster E, Vishwanath S, Kwan P. Experience of waiting for seizure freedom and perception of machine learning technologies to support treatment decision: A qualitative study in adults with recent onset epilepsy. Epilepsy Res 2023; 190:107096. [PMID: 36738538 DOI: 10.1016/j.eplepsyres.2023.107096] [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: 11/13/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023]
Abstract
PURPOSE With no reliable surrogate biomarkers for treatment response, people with epilepsy currently await the passage of time to determine whether prescribed treatments are effective. Few studies have examined the issues faced by people with epilepsy during this waiting period. We aim to explore the experiences of people with recently diagnosed epilepsy as they wait to achieve seizure freedom. METHODS We purposively sampled adults of working age who had been diagnosed and treated for epilepsy for less than four years. Semi-structured interviews were undertaken between July and September 2021. A thematic analysis using a framework approach was performed. RESULTS We recruited 15 patients. Results revealed four main themes: 1) Impact on mental health, as people with newly diagnosed epilepsy described waiting for seizure freedom as a time of vulnerability, uncertainty, and confusion. 2) Participants described their life as "on hold", prior to achieving effective seizure control 3) Difficulty navigating health systems to find and understand information about epilepsy, tests, and medications, and to find the 'right' health professional to address their needs. 4) Technology systems that support clinician decision making with selecting effective medications early after diagnosis were cautiously welcomed by participants. CONCLUSION Interventions are needed to reduce the negative impacts experienced by people who are newly diagnosed with epilepsy while waiting for effective seizure control. Technology systems that support clinician decision making were acceptable, as people with epilepsy sought accessible and effective solutions to restore a sense of control in their lives.
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Affiliation(s)
- Sandra Reeder
- School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne 3004, Australia; Department of Neurosciences, Monash University, Central Clinical School, 99 Commercial Road, Melbourne 3004, Australia.
| | - Emma Foster
- Department of Neurosciences, Monash University, Central Clinical School, 99 Commercial Road, Melbourne 3004, Australia; Department of Neurology, The Alfred, 55 Commercial Road, Melbourne 3004, Australia.
| | - Swarna Vishwanath
- School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne 3004, Australia; Department of Neurosciences, Monash University, Central Clinical School, 99 Commercial Road, Melbourne 3004, Australia.
| | - Patrick Kwan
- Department of Neurosciences, Monash University, Central Clinical School, 99 Commercial Road, Melbourne 3004, Australia; Department of Neurology, The Alfred, 55 Commercial Road, Melbourne 3004, Australia.
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25
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Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma. Cancers (Basel) 2022; 14:cancers14246231. [PMID: 36551716 PMCID: PMC9776963 DOI: 10.3390/cancers14246231] [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: 10/27/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Melanoma is among the most devastating human malignancies. Accurate diagnosis and prognosis are essential to offer optimal treatment. Histopathology is the gold standard for establishing melanoma diagnosis and prognostic features. However, discrepancies often exist between pathologists, and analysis is costly and time-consuming. Deep-learning algorithms are deployed to improve melanoma diagnosis and prognostication from histological images of melanoma. In recent years, the development of these machine-learning tools has accelerated, and machine learning is poised to become a clinical tool to aid melanoma histology. Nevertheless, a review of the advances in machine learning in melanoma histology was lacking. We performed a comprehensive literature search to provide a complete overview of the recent advances in machine learning in the assessment of melanoma based on hematoxylin eosin digital pathology images. In our work, we review 37 recent publications, compare the methods and performance of the reviewed studies, and highlight the variety of promising machine-learning applications in melanoma histology.
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26
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Chiu CC, Wu CM, Chien TN, Kao LJ, Li C, Jiang HL. Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure. J Clin Med 2022; 11:6460. [PMID: 36362686 PMCID: PMC9659015 DOI: 10.3390/jcm11216460] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 08/31/2023] Open
Abstract
Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients' conditions by healthcare professionals but allow for a more optimal use of healthcare resources.
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Affiliation(s)
- Chih-Chou Chiu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chung-Min Wu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Te-Nien Chien
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Ling-Jing Kao
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chengcheng Li
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Han-Ling Jiang
- Alliance Manchester Business School, University of Manchester, Manchester M15 6PB, UK
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27
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A digital physician peer to automatically detect erroneous prescriptions in radiotherapy. NPJ Digit Med 2022; 5:158. [PMID: 36271138 DOI: 10.1038/s41746-022-00703-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 10/07/2022] [Indexed: 11/09/2022] Open
Abstract
Appropriate dosing of radiation is crucial to patient safety in radiotherapy. Current quality assurance depends heavily on a physician peer-review process, which includes a review of the treatment plan's dose and fractionation. Potentially, physicians may not identify errors during this manual peer review due to time constraints and caseload. A novel prescription anomaly detection algorithm is designed that utilizes historical data from the past to predict anomalous cases. Such a tool can serve as an electronic peer who will assist the peer-review process providing extra safety to the patients. In our primary model, we create two dissimilarity metrics, R and F. R defining how far a new patient's prescription is from historical prescriptions. F represents how far away a patient's feature set is from that of the group with an identical or similar prescription. We flag prescription if either metric is greater than specific optimized cut-off values. We use thoracic cancer patients (n = 2504) as an example and extracted seven features. Our testing set f1 score is between 73%-94% for different treatment technique groups. We also independently validate our results by conducting a mock peer review with three thoracic specialists. Our model has a lower type II error rate compared to the manual peer-review by physicians.
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28
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Park D, Cho JM, Yang JW, Yang D, Kim M, Oh G, Kwon HD. Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms. Front Surg 2022; 9:1010420. [PMID: 36147698 PMCID: PMC9485547 DOI: 10.3389/fsurg.2022.1010420] [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: 08/03/2022] [Accepted: 08/19/2022] [Indexed: 11/28/2022] Open
Abstract
Background Therapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM. Methods This retrospective cross-sectional study included patients diagnosed with DCM, and the diagnosis of DCM was confirmed clinically and radiologically. The target outcomes were defined as conservative treatment, anterior surgical approaches (ASA), and posterior surgical approaches (PSA). We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. Two ensemble ML algorithms were used: random forest (RF) and extreme gradient boosting (XGB). The area under the receiver operating characteristic curve (AUC-ROC) was the primary metric. We also identified the variable importance for each classification. Results In total, 304 patients were included (109 conservative, 66 ASA, 125 PSA, and 4 combined surgeries). For multiclass classification, the AUC-ROC of RF and XGB models were 0.91 and 0.92, respectively. In addition, ML models showed AUC-ROC values of >0.9 for all types of binary classifications. Variable importance analysis revealed that the modified Japanese Orthopaedic Association score and central motor conduction time were the two most important variables for distinguishing between conservative and surgical treatments. When classifying ASA and PSA, the number of involved levels, age, and body mass index were important contributing factors. Conclusion ML-based classification of DCM therapeutic options is valid and feasible. This study can be a basis for establishing generalizable ML-based surgical decision models for DCM. Further studies are needed with a large multicenter database.
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Affiliation(s)
- Dougho Park
- Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Jae Man Cho
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Joong Won Yang
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Donghoon Yang
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Mansu Kim
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Gayeoul Oh
- Department of Radiology, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Heum Dai Kwon
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
- Correspondence: Heum Dai Kwon
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Dimensionality reduction of longitudinal ’omics data using modern tensor factorizations. PLoS Comput Biol 2022; 18:e1010212. [PMID: 35839259 PMCID: PMC9328521 DOI: 10.1371/journal.pcbi.1010212] [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] [Received: 02/10/2022] [Revised: 07/27/2022] [Accepted: 05/16/2022] [Indexed: 11/19/2022] Open
Abstract
Longitudinal ’omics analytical methods are extensively used in the evolving field of precision medicine, by enabling ‘big data’ recording and high-resolution interpretation of complex datasets, driven by individual variations in response to perturbations such as disease pathogenesis, medical treatment or changes in lifestyle. However, inherent technical limitations in biomedical studies often result in the generation of feature-rich and sample-limited datasets. Analyzing such data using conventional modalities often proves to be challenging since the repeated, high-dimensional measurements overload the outlook with inconsequential variations that must be filtered from the data in order to find the true, biologically relevant signal. Tensor methods for the analysis and meaningful representation of multiway data may prove useful to the biological research community by their advertised ability to tackle this challenge. In this study, we present tcam—a new unsupervised tensor factorization method for the analysis of multiway data. Building on top of cutting-edge developments in the field of tensor-tensor algebra, we characterize the unique mathematical properties of our method, namely, 1) preservation of geometric and statistical traits of the data, which enable uncovering information beyond the inter-individual variation that often takes over the focus, especially in human studies. 2) Natural and straightforward out-of-sample extension, making tcam amenable for integration in machine learning workflows. A series of re-analyses of real-world, human experimental datasets showcase these theoretical properties, while providing empirical confirmation of tcam’s utility in the analysis of longitudinal ’omics data. Tensor methods have proven useful for exploration of high-dimensional, multiway data that is produced in longitudinal ’omics studies. However, even the most recent applications of these methods to ’omics data are based on the canonical polyadic tensor-rank factorization whose results heavily depend on the choice of target rank, lack any guarantee for optimal approximation, and do not allow for out-of-sample extension in a straightforward manner. In this paper, we present a method for tensor component analysis for the analysis of longitudinal ’omics data, built on top of cutting-edge developments in the field of tensor-tensor algebra. We show that our method, in contrast to existing tensor-methods, enjoys provable optimal properties on the distortion and variance in the embedding space, enabling direct and meaningful interpretation, supporting traditional multivariate statistical analysis to be performed in the embedding space. Due to the method’s construction using tensor-tensor products, the procedure of mapping a point to the embedding space of a pre-trained factorization is simple and scalable, giving rise to the application of our method as a feature engineering step in standard machine learning workflows.
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30
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Fitzsimmons L, Dewan M, Dexheimer JW. Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications. Appl Clin Inform 2022; 13:569-582. [PMID: 35613914 DOI: 10.1055/s-0042-1749119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
OBJECTIVE As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations. METHODS We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded. RESULTS From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population. DISCUSSION With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training. CONCLUSION As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.
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Affiliation(s)
- Lane Fitzsimmons
- College of Agriculture and Life Science, Cornell University, Ithaca, New York, United States
| | - Maya Dewan
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
| | - Judith W Dexheimer
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States.,Division of Emergency Medicine; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
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31
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Zeng J, Shufean MA. Molecular-based precision oncology clinical decision making augmented by artificial intelligence. Emerg Top Life Sci 2021; 5:757-764. [PMID: 34874054 PMCID: PMC8786281 DOI: 10.1042/etls20210220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/08/2021] [Accepted: 11/16/2021] [Indexed: 01/03/2023]
Abstract
The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians' decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.
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Affiliation(s)
- Jia Zeng
- Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Md Abu Shufean
- Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
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32
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Cullin N, Azevedo Antunes C, Straussman R, Stein-Thoeringer CK, Elinav E. Microbiome and cancer. Cancer Cell 2021; 39:1317-1341. [PMID: 34506740 DOI: 10.1016/j.ccell.2021.08.006] [Citation(s) in RCA: 201] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/05/2021] [Accepted: 08/13/2021] [Indexed: 12/14/2022]
Abstract
The human microbiome constitutes a complex multikingdom community that symbiotically interacts with the host across multiple body sites. Host-microbiome interactions impact multiple physiological processes and a variety of multifactorial disease conditions. In the past decade, microbiome communities have been suggested to influence the development, progression, metastasis formation, and treatment response of multiple cancer types. While causal evidence of microbial impacts on cancer biology is only beginning to be unraveled, enhanced molecular understanding of such cancer-modulating interactions and impacts on cancer treatment are considered of major scientific importance and clinical relevance. In this review, we describe the molecular pathogenic mechanisms shared throughout microbial niches that contribute to the initiation and progression of cancer. We highlight advances, limitations, challenges, and prospects in understanding how the microbiome may causally impact cancer and its treatment responsiveness, and how microorganisms or their secreted bioactive metabolites may be potentially harnessed and targeted as precision cancer therapeutics.
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Affiliation(s)
- Nyssa Cullin
- Microbiome and Cancer Division, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Camila Azevedo Antunes
- Microbiome and Cancer Division, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Ravid Straussman
- Department of Molecular Cell Biology, Weizmann Institute of Science, 234 Herzl Street, 7610001 Rehovot, Israel
| | - Christoph K Stein-Thoeringer
- Microbiome and Cancer Division, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
| | - Eran Elinav
- Microbiome and Cancer Division, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Department of Immunology, Weizmann Institute of Science, 234 Herzl Street, 7610001 Rehovot, Israel.
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Type III secretion system effector subnetworks elicit distinct host immune responses to infection. Curr Opin Microbiol 2021; 64:19-26. [PMID: 34537517 DOI: 10.1016/j.mib.2021.08.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 01/18/2023]
Abstract
Citrobacter rodentium, a natural mouse pathogen which colonises the colon of immuno-competent mice, provides a robust model for interrogating host-pathogen-microbiota interactions in vivo. This model has been key to providing new insights into local host responses to enteric infection, including changes in intestinal epithelial cell immunometabolism and mucosal immunity. C. rodentium injects 31 bacterial effectors into epithelial cells via a type III secretion system (T3SS). Recently, these effectors were shown to be able to form multiple intracellular subnetworks which can withstand significant contractions whilst maintaining virulence. Here we highlight recent advances in understanding gut mucosal responses to infection and effector biology, as well as potential uses for artificial intelligence (AI) in understanding infectious disease and speculate on the role of T3SS effector networks in host adaption.
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Albaradei S, Thafar M, Alsaedi A, Van Neste C, Gojobori T, Essack M, Gao X. Machine learning and deep learning methods that use omics data for metastasis prediction. Comput Struct Biotechnol J 2021; 19:5008-5018. [PMID: 34589181 PMCID: PMC8450182 DOI: 10.1016/j.csbj.2021.09.001] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 08/16/2021] [Accepted: 09/02/2021] [Indexed: 12/14/2022] Open
Abstract
Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.
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Key Words
- AE, autoencoder
- ANN, Artificial Neural Network
- AUC, area under the curve
- Acc, Accuracy
- Artificial intelligence
- BC, Betweenness centrality
- BH, Benjamini-Hochberg
- BioGRID, Biological General Repository for Interaction Datasets
- CCP, compound covariate predictor
- CEA, Carcinoembryonic antigen
- CNN, convolution neural networks
- CV, cross-validation
- Cancer
- DBN, deep belief network
- DDBN, discriminative deep belief network
- DEGs, differentially expressed genes
- DIP, Database of Interacting Proteins
- DNN, Deep neural network
- DT, Decision Tree
- Deep learning
- EMT, epithelial-mesenchymal transition
- FC, fully connected
- GA, Genetic Algorithm
- GANs, generative adversarial networks
- GEO, Gene Expression Omnibus
- HCC, hepatocellular carcinoma
- HPRD, Human Protein Reference Database
- KNN, K-nearest neighbor
- L-SVM, linear SVM
- LIMMA, linear models for microarray data
- LOOCV, Leave-one-out cross-validation
- LR, Logistic Regression
- MCCV, Monte Carlo cross-validation
- MLP, multilayer perceptron
- Machine learning
- Metastasis
- NPV, negative predictive value
- PCA, Principal component analysis
- PPI, protein-protein interaction
- PPV, positive predictive value
- RC, ridge classifier
- RF, Random Forest
- RFE, recursive feature elimination
- RMA, robust multi‐array average
- RNN, recurrent neural networks
- SGD, stochastic gradient descent
- SMOTE, synthetic minority over-sampling technique
- SVM, Support Vector Machine
- Se, sensitivity
- Sp, specificity
- TCGA, The Cancer Genome Atlas
- k-CV, k-fold cross validation
- mRMR, minimum redundancy maximum relevance
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Affiliation(s)
- Somayah Albaradei
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia
| | - Maha Thafar
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Taif University, Collage of Computers and Information Technology, Taif, Saudi Arabia
| | - Asim Alsaedi
- King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
- King Abdulaziz Medical City, Jeddah, Saudi Arabia
| | - Christophe Van Neste
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Takashi Gojobori
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Magbubah Essack
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Abstract
In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. In this scenario, machine learning (ML), a subset of AI techniques, provides machines with the ability to programmatically learn from data to model a system while adapting to new situations as they learn more by data they are ingesting (on-line training). During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. The paper gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.
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