Paper Detail
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
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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@article{shueh2026diasentinel,
title = {DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening},
author = {Yung Wei Shueh and Zhi-Jie Chen and Chia-Hsuan Hsu and Hsin-Ling Hsu and Donghua Zhang and Chenwei Wu and Jun-En Ding and Tongze Zhang and Shihao Yang and Pengfei Hu and Fang-Ming Hung and Feng Liu},
year = {2026},
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 Dia},
url = {https://arxiv.org/abs/2608.31128},
keywords = {cs.CL},
eprint = {2608.31128},
archiveprefix = {arXiv},
}
{}