Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscript we illustrate a Machine Learning (ML) framework for addressing the problem of predicting Type 2 Diabetes Mellitus (T2DM) together with a solution for risk stratification, intervention and management. These ML models align with how physicians think about disease management and mitigation, which comprises these four steps: Identify, Stratify, Engage, Measure.
Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management
Qing Yuan Research Institute, SEIEE, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院清源研究院）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Qingpu Branch of Zhongshan Hospital, Affiliated to Fudan University（复旦大学附属中山医院青浦分院）
Context-Aware Optimization of Follow-Up Intervals for Type 2 Diabetes Care Using Markov Decision Processes
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；National Center for Human Factors in Healthcare（医疗人因国家中心）；Georgetown University School of Medicine（乔治城大学医学院）；Medstar Georgetown University Hospital（Medstar乔治城大学医院）
HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care
Mayo Clinic（梅奥诊所）；Imperial College London（伦敦帝国学院）；Imperial Global Singapore（新加坡帝国全球学院）；National University of Singapore（新加坡国立大学）；The University of Manchester（曼彻斯特大学）；Nanyang Technological University（南洋理工大学）
Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin
Sam Houston State University（山姆休斯顿州立大学）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas Health San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；Florida State University（佛罗里达州立大学）
The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes
Philipps-University of Marburg（马尔堡菲利普斯大学）；Weihenstephan-Triesdorf University of Applied Sciences（魏恩施蒂芬-特里斯多夫应用科学大学）；Otto-von-Guericke University Magdeburg（奥托·冯·格里克马格德堡大学）
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校信息系统与网络安全系）；School of Engineering Medicine, Texas A&M University（德克萨斯农工大学工程医学院）；Department of Family and Community Medicine, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校家庭与社区医学系）
GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Department of Endocrinology, Mayo Clinic Arizona（梅奥诊所亚利桑那分部内分泌科）
GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School for Engineering of Matter Transport and Energy, Arizona State University（亚利桑那州立大学物质传输与能源工程学院）；Institute for Foundation of Machine Learning, The University of Texas at Austin（德克萨斯大学奥斯汀分校机器学习基础研究所）；Sony AI（索尼人工智能）
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Department of Endocrinology, Mayo Clinic Arizona（梅奥诊所亚利桑那分部内分泌科）
MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes
Department of Genetics, Stanford University School of Medicine（斯坦福大学医学院遗传学系）；Replica Health；Boston Children’s Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Diabetes Research Institute, Mills-Peninsula Medical Center（米尔斯半岛医学中心糖尿病研究所）
Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes
Faculty of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Frontier Science Computing Center（前沿科学计算中心）；Chinese Academy of Sciences（中国科学院）；Chinese University of Hong Kong（香港中文大学）；Zhuhai People's Hospital（珠海人民医院）；Beijing Institute of Technology（北京理工大学）；Jinan University（暨南大学）；Lampang Inter-Tech College
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks
Harvard-MIT Health Sciences and Technology（哈佛-麻省理工学院健康科学与技术）；Massachusetts Institute of Technology (MIT)（麻省理工学院）；Massachusetts General Hospital（麻省总医院）；Columbia University（哥伦比亚大学）
Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
organization= Daniel J. Epstein Department of Industrial \& Systems Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= United States；organization= Colorectal Research Center, Iran University of Medical Sciences , city= Tehran , postcode= 14535 , country= Iran；organization= Department of Health Science, California State University , city= Long Beach , state= CA , postcode= 90840 , country= United States；organization= Department of Industrial；Manufacturing Engineering, California State Polytechnic University , city= Pomona , state= CA , postcode= 91768 , country= United States
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)
University of Florida（佛罗里达大学）；University of Florida College of Medicine（佛罗里达大学医学院）
Test-Time Learning and Inference-Time Deliberation for Efficiency-First Offline Reinforcement Learning in Care Coordination and Population Health Management
Institute of Health Informatics, University College London（伦敦大学学院健康信息学研究所）；Department of Psychiatry, University of Oxford（牛津大学精神病学系）；Department of Diabetes & Endocrinology, University College London Hospitals（伦敦大学学院医院糖尿病与内分泌科）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
