Language models are an effective representation learning technique for electronic health record data
Open accessAbstract
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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LSTM models only use structured data and are fine-tuned to forecast specific future events.
“Similar to other models, LSTM models only use structured data and are finetuned to forecast specific future events.”
From Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study (2024) · cites this paper as [7]
The data description says this study used structured medical codes and excluded clinical notes and images. Section 2.2.4 says its recurrent models were trained separately to predict specific clinical outcomes, supporting that part of the claim. But those models were GRUs, not LSTMs, and the section describes end-to-end training rather than fine-tuning; the paper does not establish that LSTM models generally use only structured data. (LLM, conf=0.85)
Experimental — accuracy not yet measuredMany LLM prompts share overlapping text segments (e.g., system messages, prompt templates, documents) that are reused frequently.
“Many input prompts have overlapping text segments, such as system messages, prompt templates, and documents provided for context.”
From Prompt Cache: Modular Attention Reuse for Low-Latency Inference (2023) · cites this paper as Steinberg et al., 2021
The Introduction and Section 2.3.4 discuss electronic health record data and a model that predicts sequences of medical codes, not LLM input prompts. Searches for overlapping text segments, system messages, prompt templates, and reused prompt prefixes found no passage addressing the claim. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences
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Clinical decision support using pseudo-notes from multiple streams of EHR data
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Prompt Cache: Modular Attention Reuse for Low-Latency Inference
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“For another example, in many legal and medical applications of LLMs (Cui et al., 2023; Steinberg et al., 2021; Rasmy et al., 2021), the same set of documents is often provided as context to different prompts.”
BackgroundImproving Diagnostics with Deep Forest Applied to Electronic Health Records
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Debiasing large language models: research opportunities*
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Generalized and transferable patient language representation for phenotyping with limited data
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Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review
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Advanced AI-based technologies for early detection and personalized management of cervical cancer
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ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in MedicinePreprint
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MedLink: De-Identified Patient Health Record Linkage
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Redefining digital health interfaces with large language models
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Soft Prompt Transfer for Zero-Shot and Few-Shot Learning in EHR Understanding
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DUGRA: Dual-Graph Representation Learning for Health Information Networks
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Integrating Genomics into Multimodal EHR Foundation ModelsPreprint
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Comprehensive survey of large models-driving intelligent decision making
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Benchmarking of pre-training strategies for electronic health record foundation models
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Self-citationGenerative Medical Event Models Improve with Scale
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Systematic review of foundation models for structured electronic health records
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Self-citationMEDS — An Emerging Data Standard and Ecosystem for Health AI Research
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Current Perspectives on the Artificial Intelligence in Critical Care Medicine
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Static and Dynamic Embedding Approaches to Identify Is-A Relations in SNOMED CT
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Using public clinical trial reports to probe non-experimental causal inference methods
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Explainable Health Risk Predictor with Transformer-based Medicare Claim EncoderPreprint
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Physiological foundation modeling for subclinical disease assessment: a prospective pilot
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Characterizing Patient Representations for Computational PhenotypingPreprint
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- Authors
- Steinberg, E., Jung, K., Fries, J. A., Corbin, C. K., Pfohl, S. R., & Shah, N. H.
- Affiliations
- Stanford University (US)
- Corresponding author
- Steinberg, Ethan
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 113
- Pages
- 103637
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC7863633
- OpenAlex
- W3112116031
- Semantic Scholar
- 349c0f1f71ba9275b9355c8c364be5fe073fc84b
- Cited by
- 132
- References
- 65
- Citations by year
- 2026: 29 · 2025: 32 · 2024: 24 · 2023: 27 · 2022: 12 · 2021: 6 · 2020: 2
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- U.S. National Library of Medicine (R01-LM011369-05)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, language models, representation learning, clinical prediction model, patient representation, transfer learning, limited training data, clinical outcome prediction
- MeSH terms
- Machine Learning, Humans, Natural Language Processing, Prognosis, Models, Statistical, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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