TIMER：面向纵向临床记录的时间指令建模与评估
TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records
AI总结：
提出TIMER框架，包含时间感知基准TIMER-Bench和指令调优方法TIMER-Instruct，通过时间指令建模提升LLM对纵向电子健康记录的时间推理能力，使模型性能在基准上提升7.3%和9.2%。
摘要：
大型语言模型（LLMs）已成为辅助医疗任务的有前景的工具，但由于电子健康记录（EHRs）的纵向特性，处理它们带来了独特的挑战。尽管LLMs执行医疗任务的能力不断提升，但其在多次患者就诊和时间范围内对时间依赖性进行推理的能力仍未被探索。我们引入了TIMER（面向纵向临床记录的时间指令建模与评估），这是一个将基于患者记录不同部分的指令-响应对作为纵向临床记录指令评估和调优关键维度的框架。我们开发了TIMER-Bench，这是首个评估纵向EHRs时间推理能力的时间感知基准，以及TIMER-Instruct，一种供LLMs学习随时间推理的指令调优方法。我们证明，使用TIMER-Instruct微调的模型在人工生成的基准上性能提升了7.3%，在TIMER-Bench上提升了9.2%，表明时间指令调优提升了模型对EHR进行推理的性能。
英文摘要：
Large language models (LLMs) have emerged as promising tools for assisting in medical tasks, yet processing Electronic Health Records (EHRs) presents unique challenges due to their longitudinal nature. While LLMs' capabilities to perform medical tasks continue to improve, their ability to reason over temporal dependencies across multiple patient visits and time frames remains unexplored. We introduce TIMER (Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records), a framework that incorporate instruction-response pairs grounding to different parts of a patient's record as a critical dimension in both instruction evaluation and tuning for longitudinal clinical records. We develop TIMER-Bench, the first time-aware benchmark that evaluates temporal reasoning capabilities over longitudinal EHRs, as well as TIMER-Instruct, an instruction-tuning methodology for LLMs to learn reasoning over time. We demonstrate that models fine-tuned with TIMER-Instruct improve performance by 7.3% on human-generated benchmarks and 9.2% on TIMER-Bench, indicating that temporal instruction-tuning improves model performance for reasoning over EHR.
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