ArticleJournal of the American Medical Informatics Association : JAMIA2024
Clinical risk prediction using language models: benefits and considerations.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.International journal of medical informatics · 2026Article
- Large language models are powerful electronic health record encoders.NPJ digital medicine · 2026Article
- Optimising pain identification in resource-limited emergency departments using transfer learning and fine-tuned language models.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study.JMIR medical informatics · 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
- Bio-Inspired Artificial Intelligence Multimorbidity Score for Human-Machine Clinical Risk Stratification.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- A large language model for delirium prediction in the intensive care unit using structured electronic health records.Scientific reports · 2025Article
- Clinical Risk Computation by Large Language Models Using Validated Risk Scores.Journal of medical systems · 2025Article
- Large language models are less effective at clinical prediction tasks than locally trained machine learning models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- Large Language Models in Medicine: Applications, Challenges, and Future Directions.International journal of medical sciences · 2025Review
- Not the Models You Are Looking For: Traditional ML Outperforms LLMs in Clinical Prediction Tasks.medRxiv : the preprint server for health sciences · 2024Article
- Large language models in biomedicine and health: current research landscape and future directions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveThe use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks.
methodsWe propose two novel LM-based methods, namely "LLaMA2-EHR" and "Sent-e-Med." Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes.
resultsExperiments across 6 different methods and 3 separate risk prediction tasks reveal that employing LMs to represent structured EHRs, such as diagnostic histories, results in significant performance improvements when evaluated using standard metrics such as area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Additionally, they offer benefits such as few-shot learning, the ability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. However, it is noteworthy that outcomes may exhibit sensitivity to a specific prompt.
conclusionLMs encompass extensive embedded knowledge, making them valuable for the analysis of EHRs in the context of risk prediction. Nevertheless, it is important to exercise caution in their application, as ongoing safety concerns related to LMs persist and require continuous consideration.
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