用大模型分析病历文本，智能减少药物不良反应误报。
HELIOT: LLM-Based CDSS for Adverse Drug Reaction Management
- 用大语言模型解析自由文本病历，自动提取用药反应信息。
- 相比传统系统，可降低超50%的干扰性提醒数量。
- 适合需要精准用药提醒的临床医生和医疗系统部署。
用药错误严重威胁患者安全，导致不良药物事件并给医疗系统带来巨大经济负担。现有的临床决策支持系统（CDSS）在处理非结构化临床数据时存在局限，常依赖静态数据库和规则算法，频繁产生过多警报，引发医护人员的警报疲劳。本文提出HELIOT，一种基于大语言模型（LLM）的新型不良药物反应管理CDSS，通过集成全面的药物数据资源库，能够处理自由文本临床信息。HELIOT利用先进的自然语言处理能力，理解医学叙述，从非结构化病历中提取相关药物反应信息，并学习过往患者的用药耐受情况，从而减少误报，实现更细致、情境化的不良药物事件预警，适用于初级诊疗、专科会诊及医院场景。初步评估使用合成临床叙事数据集与专家验证的真实数据表明，HELIOT在控制环境下表现出高准确率。通过智能分析病历中记录的既往用药耐受情况，并区分不同警报类型，其有望比传统CDSS减少超过50%的打断性提醒。尽管这些初步结果令人鼓舞，但真实世界验证仍需开展以确认其临床效益。
原文摘要 · Abstract (English)
Medication errors significantly threaten patient safety, leading to adverse drug events and substantial economic burdens on healthcare systems. Clinical Decision Support Systems (CDSSs) aimed at mitigating these errors often face limitations when processing unstructured clinical data, including reliance on static databases and rule-based algorithms, frequently generating excessive alerts that lead to alert fatigue among healthcare providers. This paper introduces HELIOT, an innovative CDSS for adverse drug reaction management that processes free-text clinical information using Large Language Models (LLMs) integrated with a comprehensive pharmaceutical data repository. HELIOT leverages advanced natural language processing capabilities to interpret medical narratives, extract relevant drug reaction information from unstructured clinical notes, and learn from past patient-specific medication tolerances to reduce false alerts, enabling more nuanced and contextual adverse drug event warnings across primary care, specialist consultations, and hospital settings. An initial evaluation using a synthetic dataset of clinical narratives and expert-verified ground truth shows promising results. HELIOT achieves high accuracy in a controlled setting. In addition, by intelligently analyzing previous medication tolerance documented in clinical notes and distinguishing between cases requiring different alert types, HELIOT can potentially reduce interruptive alerts by over 50% compared to traditional CDSSs. While these preliminary findings are encouraging, real-world validation will be essential to confirm these benefits in clinical practice.
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