@inproceedings{shueh-etal-2026-diasentinel,
title = "{DIASENTINEL}: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening",
author = "Shueh, Yung Wei and
Chen, Zhi-Jie and
Hsu, Chia-Hsuan and
Hsu, Hsin-Ling and
Zhang, Donghua and
Wu, Chenwei and
Ding, Jun-En and
Zhang, Tongze and
Yang, Shihao and
Hu, Pengfei and
Hung, Fang Ming and
Liu, Feng",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.32/",
pages = "320--329",
ISBN = "979-8-89176-461-3",
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. Our system demonstration can be found at https://diasentinel-demo.onrender.com/"
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%0 Conference Proceedings
%T DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening
%A Shueh, Yung Wei
%A Chen, Zhi-Jie
%A Hsu, Chia-Hsuan
%A Hsu, Hsin-Ling
%A Zhang, Donghua
%A Wu, Chenwei
%A Ding, Jun-En
%A Zhang, Tongze
%A Yang, Shihao
%A Hu, Pengfei
%A Hung, Fang Ming
%A Liu, Feng
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F shueh-etal-2026-diasentinel
%X 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. Our system demonstration can be found at https://diasentinel-demo.onrender.com/
%U https://aclanthology.org/2026.emnlp-demos.32/
%P 320-329
Markdown (Informal)
[DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening](https://aclanthology.org/2026.emnlp-demos.32/) (Shueh et al., EMNLP 2026)
ACL
- 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, and Feng Liu. 2026. DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 320–329, Budapest, Hungary. Association for Computational Linguistics.