ArticleJournal of biomedical informatics2021
Language models are an effective representation learning technique for electronic health record data.
Article in Journal of biomedical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Article
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Large language models are powerful electronic health record encoders.NPJ digital medicine · 2026Article
- Article
- Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records.Nature. Mental health · 2026Article
- Comparative Evaluation of Proprietary and Open-Source Large Language Models for Systematic Multi-source Information Extraction in Interventional Oncology.Cardiovascular and interventional radiology · 2026Article
- Disease Risk Prediction Using Structured EHR Data: Can Generalist Large Language Models Match Specialized Clinical Foundation Models? A Comparative Evaluation with Fine-Tuning.medRxiv : the preprint server for health sciences · 2026Article
- A Foundation Model for Capturing Complexity of Menstrual Health Data.npj women's health · 2026Article
- Augmenting Electronic Health Records for Adverse Event Detection.medRxiv : the preprint server for health sciences · 2026Article
- Physiological foundation modeling for subclinical disease assessment: a prospective pilot.JAMIA open · 2026Article
- Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Evaluation of Large Language Models as Emergency Department Revisit Predictors.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- A multimodal generative model for structured and unstructured electronic health records.npj health systems · 2026Article
- A method for characterizing disease progression from acute kidney injury to chronic kidney disease.Journal of biomedical informatics · 2025Article
- Article
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.BMJ health & care informatics · 2025Article
- Benchmarking of pre-training strategies for electronic health record foundation models.JAMIA open · 2025Article
- Clinical decision support using pseudo-notes from multiple streams of EHR data.NPJ digital medicine · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. However, this process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant. Such patient representation schemes enable a 3.5% mean improvement in AUROC on five prediction tasks compared to standard baselines, with the average improvement rising to 19% when only a small number of patient records are available for training the clinical prediction model.
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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.