TIMER: temporal instruction modeling and evaluation for longitudinal clinical records.

ABSTRACT: Electronic health records (EHRs) contain rich longitudinal information for clinical decision-making, yet LLMs struggle to reason across patient timelines. We introduce TIMER (Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records), a method to improve LLMs' temporal reasoning over multi-visit EHRs through time-aware instruction tuning. TIMER grounds LLMs in patient-specific temporal contexts by linking each instruction-response pair to specific timestamps, ensuring temporal fidelity throughout the training process. Evaluations show that TIMER-tuned models outperform conventional medical instruction-tuned approaches by 6.6% in completeness on clinician-curated benchmarks, with distribution-matched training demonstrating advantages up to 6.5% in temporal reasoning. Qu

SUBMITTER: Cui H 

PROVIDER: S-EPMC12475073 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature