Keywords
Decision Support System
Diabetes Mellitus
Deep Learning
Reinforcement Learning
How to Cite
Abstract
Purpose: This paper presents a Generative Artificial Intelligence (GenAI)-based Decision Support System (DSS) framework for the early prediction and clinical management of diabetes mellitus. The study aims to integrate generative modeling, counterfactual simulation, and reinforcement learning into a unified architecture to enhance diagnostic precision, explainability, and personalized treatment recommendations.
Design/Methodology/Approach: The proposed Generative Decision Support Model (GDSM) employs multimodal generative pre-training (GMP) using longitudinal Electronic Health Records (EHRs), combining structured and unstructured data such as laboratory investigations, prescriptions, and clinical narratives. A transformer-based generative backbone synthesizes patient trajectories through cross-modal representation learning, temporal sparse attention, and contrastive alignment. A world model simulator enables counterfactual reasoning for evaluating hypothetical clinical interventions, while the policy optimization layer applies conservative offline reinforcement learning to generate safe and actionable recommendations.
Findings: Experimental results demonstrate that the GDSM significantly improves early diabetes prediction accuracy and clinical interpretability compared to conventional rule-based or discriminative models. By simulating patient responses and optimizing treatment strategies, the framework enhances data efficiency, calibration, and reliability of predictive outcomes. The integration of SHAP-based explainability and Human-in-the-Loop (HITL) feedback mechanisms increases model transparency and clinician trust.
Practical Implications: The framework enables personalized diabetes care through predictive simulation and ethically grounded AI decision support. It also establishes a scalable pathway for generative AI adoption in clinical informatics and chronic disease management.
Originality/Value: This study introduces a next-generation generative DSS architecture that transitions from predictive analytics to proactive, interpretable, and patient-centered clinical decision-making.
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