Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management
This work addresses the problem of improving healthcare management for Type 2 Diabetes patients, but it is incremental as it primarily offers an overview rather than new methods.
The paper tackles the lack of a comprehensive overview for diabetes prediction and care management by providing a framework that integrates prediction, risk stratification, intervention, and management, based on real-world clinical experience.
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.