DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening
2026-08-31 • Computation and Language
Computation and Language
AI summaryⓘ
The authors created DIASENTINEL, a tool that helps doctors screen for type 2 diabetes risk using patient health records. It combines smart predictions, clear clinical rule checks, and trustworthy guidelines from the American Diabetes Association. Their system also verifies its recommendations to avoid mistakes and keeps all data private by running locally. This approach shows how large language models can be used carefully and reliably in healthcare.
large language modelstype 2 diabetes mellituselectronic health recordsclinical decision supportrisk predictionAmerican Diabetes Association guidelinesrule-based verificationReciprocal Rank Fusionprivacy-preserving systems
Authors
Yung Wei Shueh, Zhi-Jie Chen, Chia-Hsuan Hsu, Hsin-Ling Hsu, Donghua Zhang, Chenwei Wu, Jun-En Ding, Tongze Zhang, Shihao Yang, Pengfei Hu, Fang-Ming Hung, Feng Liu
Abstract
Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors. We present DIASENTINEL, a fully on-premise multi-agent system for one-year type 2 diabetes mellitus (T2DM) risk screening and guideline-grounded report generation from electronic health records (EHRs). The system integrates calibrated risk prediction, deterministic clinical signal extraction, Reciprocal Rank Fusion over American Diabetes Association (ADA) guidelines, and a hybrid verification layer combining rule-based checks with LLM entailment. The demonstration provides a real-time batch-screening dashboard and an interactive patient report interface with cited recommendations, verification results, and raw EHR comparison. DIASENTINEL demonstrates a practical framework for reliable, auditable, and privacy-preserving LLM-based clinical decision support.