用病历文本训练大模型预测未来临床事件，无需人工标注结构化特征。
Training Large Language Models to Predict Clinical Events
- 将时间序列病历转为自然语言问答形式，构建预测任务数据集。
- 微调后模型校准误差降为0.0398，Brier得分降至0.145，优于基线。
- 适用于医疗预测场景，尤其适合缺乏标注数据的临床研究者。
纵向临床记录蕴含患者随时间演变的丰富信息，但将其转化为临床预测的训练监督仍具挑战。本文通过将时间有序的MIMIC-III病历转化为包含过去患者上下文、关于未来可能事件的自然语言问题及后期文档确认标签的样本，构建了6,900个预测示例，覆盖702次住院记录中的药物、操作、器官支持、微生物学检测和死亡等事件。使用小规模LoRA适配器在这些示例上微调，使模型预期校准误差从0.1269降至0.0398，Brier得分从0.199降至0.145，且在保留问题上的表现略优于GPT-5点估计。该方法可在不依赖人工设计结构特征或特定终点分类器的前提下，实现可复用的临床预测监督。
原文摘要 · Abstract (English)
Longitudinal clinical notes contain rich evidence of how patients evolve over time, but converting this signal into training supervision for clinical prediction remains challenging. We extend Foresight Learning to clinical prediction by converting time-ordered MIMIC-III notes into examples consisting of past patient context, a natural-language question about a possible future event, and a label resolved from later documentation. This process yields 6,900 prediction examples from 702 admissions across medications, procedures, organ support, microbiology, and mortality. A small LoRA adapter trained on these examples improves over the prompted base model, reducing expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145, while slightly outperforming GPT-5 point estimates on held-out questions. The approach enables reusable clinical prediction supervision from longitudinal notes without hand-engineered structured features or endpoint-specific classifiers.
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