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Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning

reinforcement-learning › offline-rl

📄 Abstract

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.

Authors (1)

Yuhan Tang

Submitted

October 19, 2025

arXiv Category

stat.AP

arXiv PDF

Key Contributions

This paper proposes an offline contextual bandit approach using a mixed-action Soft Actor-Critic (SAC) algorithm to learn individualized lifestyle prescriptions for Type 2 diabetes prevention and treatment. By minimizing a glucose risk-reward function on aggregated NHANES data, the model generates personalized recommendations, addressing the limitations posed by a shortage of trained professionals and variability in physician expertise.

Business Value

Potential to scale personalized diabetes care by automating lifestyle prescription generation, improving patient adherence and health outcomes, and reducing healthcare system costs.

Paper Metadata

Innovation Type

Methodological

Deployment Feasibility

Moderate. Requires robust data infrastructure for accessing and processing large health datasets. Integration into clinical decision support systems is feasible.

Limitations Addressed

Shortage of trained professionals for personalized lifestyle medicine,Variability in physicians' expertise,Difficulty in generating personalized prescriptions from aggregated data

Performance Gains

The model generates risk-aware lifestyle medicine prescriptions comparable to those issued by certified physicians.

Technical Tags

Reinforcement LearningOffline RLContextual BanditsLifestyle MedicineType 2 DiabetesPersonalized PrescriptionsSoft Actor-CriticNHANES dataRisk-reward functionMixed-action policies

Research Topics

Diabetes ManagementPersonalized MedicineReinforcement LearningPublic HealthData-driven Interventions

Methods & Architectures

Offline contextual bandit approachSoft Actor-Critic (SAC) algorithmMinimizing Magni glucose risk-reward function Soft Actor-Critic

Applications & Tasks

Diabetes Prevention and Treatment Personalized Health Recommendations Generating personalized lifestyle prescriptionsOptimizing health outcomes using historical dataAddressing shortage of trained professionals Prescribing personalized lifestyle interventions for Type 2 DiabetesMinimizing glucose risk-reward

Datasets & Benchmarks

Datasets

NHANES (National Health and Nutrition Examination Survey)

Minimization of Magni glucose risk-reward functionComparison with physician-issued prescriptions

Related Fields

Public HealthEndocrinologyHealth InformaticsMachine LearningBehavioral Economics

Keywords

Type 2 Diabeteslifestyle medicinereinforcement learningoffline RLcontextual banditspersonalized prescriptionsNHANESSoft Actor-Critichealth outcomespublic healthrisk-rewardmixed-actiondiabetes preventiontreatmentAI

Academic Context

#Diabetes Management#Personalized Medicine#Reinforcement Learning#Public Health#Data-driven Interventions

Companies & Organizations

Companies Mentioned

Xiangya Hospital

Technology Stack

Frameworks & Libraries

Soft Actor-Critic (SAC) algorithm

Commercial Potential

Potential Products

AI-powered diabetes management platformPersonalized health coaching appClinical decision support tool for physicians

Target Industries

HealthcareDigital HealthPharmaceuticalsInsurance

Use Case Examples

A patient receives AI-generated weekly meal and exercise plans tailored to their specific diabetes profile and risk factors.A healthcare provider uses the system to quickly generate evidence-based lifestyle recommendations for patients.

Competitive Edge

Offers an automated, data-driven approach to personalized lifestyle prescriptions, potentially more consistent and scalable than manual physician recommendations.

Market Opportunity

Massive global market for diabetes management and prevention solutions.

Revenue Models

Subscription services for patients/providerslicensing to EMR systemspartnerships with health insurance companies.

Resource Requirements

Compute Needs

Moderate to high for training the SAC model on large datasets like NHANES.

Data Requirements

Large-scale, cross-sectional health survey data (like NHANES) with patient profiles and health indicators.

Deployment Constraints

Ethical considerations for AI-driven health advice,Regulatory approval for medical applications,Need for patient trust and engagement

Scalability

The offline RL approach is inherently scalable as it learns from existing data, allowing for broad application once trained.

Regulatory Considerations

Potential classification as a medical devicerequiring FDA or equivalent approval.

Production Readiness

Maturity Level

Research

Time to Market

3-5 years for a clinically validated and approved product.

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