ArticleNPJ digital medicine2026
Large language models improve transferability of electronic health record-based predictions across countries and coding systems.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- A Supervised Text-Embedded Transformer Matching Model to Detect Fall Injuries in Medicare Data.medRxiv : the preprint server for health sciences · 2026Article
- Large language models are powerful electronic health record encoders.NPJ digital medicine · 2026Article
- Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency.Vox sanguinis · 2026Review
- About Digitalisation and AI, Data Protection, Data Exchange, Data Mining-Legal Constraints/Challenges Concerning Sleep Medicine.Journal of sleep research · 2025Review
Corrections and comments
- Update of
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Variation in medical practices and reporting standards across healthcare systems limits the transferability of prediction models based on structured electronic health record data. Prior studies have demonstrated that embedding medical codes into a shared semantic space can help address these discrepancies, but real-world applications remain limited. Here, we show that leveraging embeddings from a large language model alongside a transformer-based prediction model provides an effective and scalable solution to enhance generalizability. We call this approach GRASP and apply it to predict the onset of 21 diseases and all-cause mortality in over one million individuals. Trained on the UK Biobank (UK) and evaluated in FinnGen (Finland) and Mount Sinai (USA), GRASP achieved an average ΔC-index that was 88% and 47% higher than language-unaware models, respectively. GRASP also showed significantly higher correlations with polygenic risk scores for 62% of diseases, and maintained robust performance even when datasets were not harmonized to the same data model.
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.