Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning

TL;DR

This paper introduces an offline reinforcement learning approach to generate personalized lifestyle medicine prescriptions for type 2 diabetes management, aiming to overcome professional shortages and variability in expertise.

Contribution

It presents a novel offline contextual bandit model using a mixed-action Soft Actor-Critic algorithm trained on NHANES data for personalized diabetes lifestyle prescriptions.

Findings

Model generates risk-aware prescriptions comparable to physicians.

Approach validated against prescriptions from certified physicians.

Demonstrates potential for scalable, personalized diabetes care.

Abstract

Type 2 diabetes prevention and treatment can benefit from personalized lifestyle prescriptions. However, the delivery of personalized lifestyle medicine prescriptions is limited by the shortage of trained professionals and the variability in physicians' expertise. We propose an offline contextual bandit approach that learns individualized lifestyle prescriptions from the aggregated NHANES profiles of 119,555 participants by minimizing the Magni glucose risk-reward function. The model encodes patient status and generates lifestyle medicine prescriptions, which are trained using a mixed-action Soft Actor-Critic algorithm. The task is treated as a single-step contextual bandit. The model is validated against lifestyle medicine prescriptions issued by three certified physicians from Xiangya Hospital. These results demonstrate that offline mixed-action SAC can generate risk-aware lifestyle medicine prescriptions from cross-sectional NHANES data, warranting prospective clinical validation.