用大模型从病例报告中提取事件与时间点，构建可分析的时序数据。
A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports
- 基于大模型自动标注病例报告中的事件与时间，生成文本时序对。
- 模型事件召回率达80%，事件时间一致性高达95%。
- 适合做临床轨迹分析、因果推理的研究者使用。
临床事件发生的时间是刻画患者病程的关键，有助于过程追踪、预测和因果推断。然而，结构化电子健康记录中很少包含此类关键信息，而临床报告也缺乏事件的时间定位。我们提出一个系统，将病例报告转化为包含文本事件与时间戳的结构化时序对。对比人工标注（n=320）与大语言模型标注（n=390）在10篇随机抽取的开放获取PubMed病例报告（N=152,974）上的结果，并评估模型间一致性（n=3,103；N=93）。结果显示，大模型在事件召回率上表现中等（O1-preview: 0.80），但已识别事件的时间一致性极高（O1-preview: 0.95）。通过建立任务定义、标注与评估体系，本研究为利用PMOA语料库开展时序分析提供了基准。
原文摘要 · Abstract (English)
Timing of clinical events is central to characterization of patient trajectories, enabling analyses such as process tracing, forecasting, and causal reasoning. However, structured electronic health records capture few data elements critical to these tasks, while clinical reports lack temporal localization of events in structured form. We present a system that transforms case reports into textual time series-structured pairs of textual events and timestamps. We contrast manual and large language model (LLM) annotations (n=320 and n=390 respectively) of ten randomly-sampled PubMed open-access (PMOA) case reports (N=152,974) and assess inter-LLM agreement (n=3,103; N=93). We find that the LLM models have moderate event recall(O1-preview: 0.80) but high temporal concordance among identified events (O1-preview: 0.95). By establishing the task, annotation, and assessment systems, and by demonstrating high concordance, this work may serve as a benchmark for leveraging the PMOA corpus for temporal analytics.
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