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arXiv · 2608.31128

DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening

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
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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.

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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. 2026-08-31. DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening. https://arxiv.org/abs/2608.31128

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