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Guo Q, Fu B, Tian Y, Xu S, Meng X. Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development. Curr Med Res Opin 2024; 40:1483-1493. [PMID: 39083361 DOI: 10.1080/03007995.2024.2387187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024]
Abstract
Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.
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Affiliation(s)
- Qi Guo
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Bo Fu
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Yuan Tian
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Shujun Xu
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Xin Meng
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
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Wändell P, Carlsson AC, Wierzbicka M, Sigurdsson K, Ärnlöv J, Eriksson J, Wachtler C, Ruge T. A machine learning tool for identifying patients with newly diagnosed diabetes in primary care. Prim Care Diabetes 2024:S1751-9918(24)00123-2. [PMID: 38944562 DOI: 10.1016/j.pcd.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/01/2024]
Abstract
BACKGROUND AND AIM It is crucial to identify a diabetes diagnosis early. Create a predictive model utilizing machine learning (ML) to identify new cases of diabetes in primary health care (PHC). METHODS A case-control study utilizing data on PHC visits for sex-, age, and PHC-matched controls. Stochastic gradient boosting was used to construct a model for predicting cases of diabetes based on diagnostic codes from PHC consultations during the year before index (diagnosis) date and number of consultations. Variable importance was estimated using the normalized relative influence (NRI) score. Risks of having diabetes were calculated using odds ratios of marginal effects (ORME). Four groups by age and sex were studied, age-groups 35-64 years and ≥ 65 years in men and women, respectively. RESULTS The most important predictive factors were hypertension with NRI 21.4-29.7 %, and obesity 4.8-15.2 %. The NRI for other top ten diagnoses and administrative codes generally ranged 1.0-4.2 %. CONCLUSIONS Our data confirm the known risk patterns for predicting a new diagnosis of diabetes, and the need to test blood glucose frequently. To assess the full potential of ML for risk prediction purposes in clinical practice, future studies could include clinical data on life-style patterns, laboratory tests and prescribed medication.
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Affiliation(s)
- Per Wändell
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden
| | - Axel C Carlsson
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden; Academic Primary Health Care Centre, Region Stockholm, Stockholm, Sweden.
| | - Marcelina Wierzbicka
- Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden
| | - Karolina Sigurdsson
- Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden; School of Health and Social Studies, Dalarna University, Falun, Sweden
| | - Julia Eriksson
- Division of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Caroline Wachtler
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden
| | - Toralph Ruge
- Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden
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Yousef H, Feng SF, Jelinek HF. Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers. Sci Rep 2024; 14:14409. [PMID: 38909127 PMCID: PMC11193708 DOI: 10.1038/s41598-024-65044-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 06/17/2024] [Indexed: 06/24/2024] Open
Abstract
Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.
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Affiliation(s)
- Hibba Yousef
- Biotechnology Research Center, Technology Innovation Institute, Masdar City, P. O. Box 9639, Abu Dhabi, United Arab Emirates.
| | - Samuel F Feng
- Department of Science and Engineering, Sorbonne University Abu Dhabi, Abu Dhabi, United Arab Emirates
- SUAD Research Institute, Sorbonne University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Herbert F Jelinek
- Department of Medical Sciences, Khalifa University, 127788, Abu Dhabi, United Arab Emirates
- Biotechnology Center, Khalifa University, 127788, Abu Dhabi, United Arab Emirates
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Chigusa Y. How does the precise prediction of preeclampsia onset aid the overall management of preeclampsia? Hypertens Res 2024; 47:1420-1421. [PMID: 38409509 DOI: 10.1038/s41440-024-01621-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 01/22/2024] [Accepted: 01/27/2024] [Indexed: 02/28/2024]
Affiliation(s)
- Yoshitsugu Chigusa
- Department of Gynecology and Obstetrics, Kyoto University Hospital, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
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Yan JK. A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients. PeerJ Comput Sci 2024; 10:e2017. [PMID: 38855224 PMCID: PMC11157615 DOI: 10.7717/peerj-cs.2017] [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: 11/22/2023] [Accepted: 04/03/2024] [Indexed: 06/11/2024]
Abstract
The scarcity of data is likely to have a negative effect on machine learning (ML). Yet, in the health sciences, data is diverse and can be costly to acquire. Therefore, it is critical to develop methods that can reach similar accuracy with minimal clinical features. This study explores a methodology that aims to build a model using minimal clinical parameters to reach comparable performance to a model trained with a more extensive list of parameters. To develop this methodology, a dataset of over 1,000 COVID-19-positive patients was used. A machine learning model was built with over 90% accuracy when combining 24 clinical parameters using Random Forest (RF) and logistic regression. Furthermore, to obtain minimal clinical parameters to predict the mortality of COVID-19 patients, the features were weighted using both Shapley values and RF feature importance to get the most important factors. The six most highly weighted features that could produce the highest performance metrics were combined for the final model. The accuracy of the final model, which used a combination of six features, is 90% with the random forest classifier and 91% with the logistic regression model. This performance is close to that of a model using 24 combined features (92%), suggesting that highly weighted minimal clinical parameters can be used to reach similar performance. The six clinical parameters identified here are acute kidney injury, glucose level, age, troponin, oxygen level, and acute hepatic injury. Among those parameters, acute kidney injury was the highest-weighted feature. Together, a methodology was developed using significantly minimal clinical parameters to reach performance metrics similar to a model trained with a large dataset, highlighting a novel approach to address the problems of clinical data collection for machine learning.
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Khera R, Simon MA, Ross JS. Automation Bias and Assistive AI: Risk of Harm From AI-Driven Clinical Decision Support. JAMA 2023; 330:2255-2257. [PMID: 38112824 DOI: 10.1001/jama.2023.22557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut
- Associate Editor, JAMA
| | - Melissa A Simon
- Associate Editor, JAMA
- Department of Obstetrics and Gynecology, Northwestern Medicine Feinberg School of Medicine, Chicago, Illinois
| | - Joseph S Ross
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut
- Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Deputy Editor, JAMA
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Thangaraj PM, Khera R. Accelerating chest pain evaluation with machine learning. EUROPEAN HEART JOURNAL. ACUTE CARDIOVASCULAR CARE 2023; 12:753-754. [PMID: 37793075 PMCID: PMC11004857 DOI: 10.1093/ehjacc/zuad117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 10/06/2023]
Affiliation(s)
- Phyllis M Thangaraj
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 330 Cedar Street, Boardman 110, New Haven, CT 06520, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 330 Cedar Street, Boardman 110, New Haven, CT 06520, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06510, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, Floor 9, New Haven, CT 06510, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church Street, 6th Floor, New Haven, CT 06510, USA
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Shankar SV, Oikonomou EK, Khera R. CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.02.23296404. [PMID: 37873174 PMCID: PMC10593062 DOI: 10.1101/2023.10.02.23296404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
In the rapidly evolving landscape of modern healthcare, the integration of wearable and portable technology provides a unique opportunity for personalized health monitoring in the community. Devices like the Apple Watch, FitBit, and AliveCor KardiaMobile have revolutionized the acquisition and processing of intricate health data streams that were previously accessible only through devices only available to healthcare providers. Amidst the variety of data collected by these gadgets, single-lead electrocardiogram (ECG) recordings have emerged as a crucial source of information for monitoring cardiovascular health. Notably, there has been significant advances in artificial intelligence capable of interpreting these 1-lead ECGs, facilitating clinical diagnosis as well as the detection of rare cardiac disorders. This design study describes the development of an innovative multi-platform system aimed at the rapid deployment of AI-based ECG solutions for clinical investigation and care delivery. The study examines various design considerations, aligning them with specific applications, and develops data flows to maximize efficiency for research and clinical use. This process encompasses the reception of single-lead ECGs from diverse wearable devices, channeling this data into a centralized data lake, and facilitating real-time inference through AI models for ECG interpretation. An evaluation of the platform demonstrates a mean duration from acquisition to reporting of results of 33.0 to 35.7 seconds, after a standard 30 second acquisition, allowing the complete process to be completed in 63.0 to 65.7 seconds. There were no substantial differences in acquisition to reporting across two commercially available devices (Apple Watch and KardiaMobile). These results demonstrate the succcessful translation of design principles into a fully integrated and efficient strategy for leveraging 1-lead ECGs across platforms and interpretation by AI-ECG algorithms. Such a platform is critical to translating AI discoveries for wearable and portable ECG devices to clinical impact through rapid deployment.
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Affiliation(s)
- Sumukh Vasisht Shankar
- Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Evangelos K Oikonomou
- Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Rohan Khera
- Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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