ArticleNPJ digital medicine2025
TIMER: temporal instruction modeling and evaluation for longitudinal clinical records.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Critical Care-Specific vs General-Purpose Large Language Models in Emergency Intensive Care Unit Diagnosis: Single-Center Retrospective Paired Comparative Study.Journal of medical Internet research · 2026Article
- Personal Health Large Language Models and the Negotiation of Medical Authority in Clinical Care: Opportunities, Risks, and Governance.Journal of medical Internet research · 2026Article
- Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.medRxiv : the preprint server for health sciences · 2026Article
- BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.Nature biomedical engineering · 2026Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- TempReasoner: neural temporal graph networks for event timeline construction.Scientific reports · 2026Article
- Clinical temporal relation extraction with long-context transformers: a robustness study on MIMIC-III and MIMIC-IV.Frontiers in digital health · 2026Article
- Article
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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. Qualitative analyses reveal that using TIMER enhances temporal boundary adherence, trend detection, and chronological precision, necessary for applications such as disease trajectory modeling and treatment response monitoring. Overall, TIMER provides a methodological basis for developing LLMs that can effectively engage with the inherently longitudinal nature of data for patient care. Code is available at TIMER .
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.