基于指南的糖尿病风险筛查系统，确保生成报告可审计且隐私安全。
DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening
- 多智能体架构融合指南检索与规则校验，保障决策可信
- 在真实EHR数据上实现1年期糖尿病风险预测，支持批量筛查
- 适合医疗AI落地场景，尤其关注可解释性与合规性的团队
大型语言模型（LLMs）在临床决策支持中展现潜力，但存在幻觉、无依据推荐和引用错误等风险。本文提出DIASENTINEL，一个完全本地部署的多智能体系统，用于从电子健康记录（EHRs）中进行一年期2型糖尿病（T2DM）风险筛查，并生成基于指南的报告。系统集成校准的风险预测、确定性临床信号提取、基于美国糖尿病协会（ADA）指南的互斥排名融合，以及结合规则检查与LLM蕴含判断的混合验证层。演示包含实时批处理筛查仪表盘和交互式患者报告界面，展示引用建议、验证结果及原始EHR对比。DIASENTINEL展示了可靠、可审计且隐私保护的LLM临床决策支持实用框架。
原文摘要 · Abstract (English)
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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