用大模型从病历报告中提取时间序列的感染性休克信息，构建了首个开放文本时序数据集。
Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis
- 用大模型自动识别病历中的临床事件并标注时间点
- 在2139份报告上实现93%事件匹配率和96.5%时间顺序一致率
- 适合医疗人工智能、临床时间建模研究者使用
临床病例报告和出院小结可能是患者诊疗过程最完整准确的总结，但通常在诊疗结束后才完成，缺乏实时性。而结构化数据虽能及时获取，却存在不完整问题。为训练更全面、时间粒度更细的模型，我们构建了一套基于大语言模型的流程，用于对病例报告进行表型识别、信息提取与时间定位标注。该方法应用于包含2,139份病例报告的Sepsis-3数据集（来自PubMed-Open Access子集），生成了一个开源文本时序语料库。通过与i2b2/MIMIC-IV的时间线标注及医生专家标注对比验证，结果表明：GPT-5事件匹配率达0.93，时间一致性达0.965；Llama 3.3 70B Instruct分别达到0.76和0.908。研究揭示了大模型在文本中时间定位临床发现的能力边界，并提出通过多模态融合改进的潜在路径。
原文摘要 · Abstract (English)
Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i.e., timestamped after the encounter. Complementary structured data streams become available sooner but suffer from incompleteness. To train models and algorithms on more complete and temporally fine-grained data, we construct a pipeline to phenotype, extract, and annotate time-localized findings within case reports using large language models. We apply our pipeline to generate an open-access textual time series corpus for Sepsis-3 comprising 2,139 case reports from the PubMed-Open Access (PMOA) Subset. To validate our system, we apply it to PMOA and timeline annotations from i2b2/MIMIC-IV and compare the results to physician-expert annotations. We show high recovery rates of clinical findings (event match rates: GPT-5--0.93, Llama 3.3 70B Instruct--0.76) and strong temporal ordering (concordance: GPT-5--0.965, Llama 3.3 70B Instruct--0.908). Our work characterizes the ability of LLMs to time-localize clinical findings in text, illustrating the limitations of LLM use for temporal reconstruction and providing several potential avenues of improvement via multimodal integration.
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