发表机构
Far Eastern Memorial Hospital; University of Michigan; Rutgers University; Stevens Institute of Technology（远东纪念医院; 密歇根大学; 罗格斯大学; 史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出DIASENTINEL多智能体系统，用于基于EHR和ADA指南的2型糖尿病风险筛查与报告，整合多种技术实现可靠可审计的临床决策支持，解决LLM临床应用的幻觉等问题。
AI 中文摘要
大型语言模型（LLMs）为临床决策支持提供了广阔前景，但仍易出现事实幻觉、无依据建议及引用错误。我们提出DIASENTINEL，一种完全本地部署的多智能体系统，用于基于电子健康记录（EHRs）的1年期2型糖尿病（T2DM）风险筛查及基于指南的报告生成。该系统整合了校准风险预测、确定性临床信号提取、针对美国糖尿病协会（ADA）指南的 reciprocal rank fusion（ reciprocal rank融合），以及结合基于规则的检查与LLM蕴含验证的混合验证层。演示提供了实时批量筛查仪表板和交互式患者报告界面，包含引用建议、验证结果及原始EHR对比。DIASENTINEL展示了一种可靠、可审计且隐私保护的基于LLM的临床决策支持实用框架。
英文摘要
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.
