2021/04/15 by Aloysius Lim, Ashish Singh, Lim, Aloysius +11
Computer Science · Health Professions · Mathematics · Medicine · #Artificial Intelligence in Healthcare #Diabetes Management and Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2104.07820
openalex publication_date 2021/04/15 · arxiv created 2021/04/29 · arxiv updated 2021/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.