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

2026-08-31

AI Summary

DIASENTINEL系统通过集成校准风险预测、临床信号提取等方法，解决LLMs在糖尿病风险筛查中可能出现的事实错误等问题。

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.

Problem

• Large language models
• clinical decision support
• hallucinated facts
• type 2 diabetes mellitus
• guideline-grounded

Innovation

• Multi-Agent System
• Guideline-Grounded
• Hybrid Verification Layer
• Privacy-Preserving
• Clinical Decision Support