Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management

• URL: http://arxiv.org/abs/2104.07820v1
• Date: Thu, 15 Apr 2021 23:38:39 GMT
• Title: Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management
• Authors: Aloysius Lim, Ashish Singh, Jody Chiam, Carly Eckert, Vikas Kumar, Muhammad Aurangzeb Ahmad, Ankur Teredesai
• Abstract summary: This document seeks to remedy an omission in literature with an encompassing overview of diabetes complication prediction. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying Machine Learning (ML) models.
• Score: 15.15357567896085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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