ArticleNPJ digital medicine2025
Transformer patient embedding using electronic health records enables patient stratification and progression analysis.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Article
- Explainable SHAP-space fall risk profiling from electronic health records for inpatient prevention planning.npj health systems · 2026Article
- Uncertainty-calibrated adaptation of clinical transformer foundation models enhances in-hospital mortality and hospital readmission prediction.npj health systems · 2026Article
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction.Scientific reports · 2026Article
- OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology.bioRxiv : the preprint server for biology · 2026Article
- Multimorbidity quantification from the perspective of precision management: challenges and strategies.Annals of medicine · 2025Review
- PANCDetect: Early Detection of Pancreatic Cancer from Multimodal EHR data with LLM Embeddings.medRxiv : the preprint server for health sciences · 2025Article
- Artificial intelligence for autoimmune diseases.NPJ digital medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Current studies regarding the secondary use of electronic health records (EHR) predominantly rely on domain expertise and existing medical knowledge. A powerful representation approach can unleash the potential of discovering new medical patterns underlying the EHR. Here, we introduce an unsupervised method for embedding high-dimensional EHR data at the patient level to characterize heterogeneity in complex diseases and identify novel disease patterns linked to disparities in clinical outcomes. We applied this approach to 34,851 unique medical codes across 1,046,649 longitudinal patient events, including 102,740 patients in the Electronic Medical Records and GEnomics (eMERGE) Network. The model achieved strong predictive performance in predicting future disease (median AUROC = 0.87 within one year) and bulk phenotyping (median AUROC = 0.84). Notably, these patient embeddings revealed diverse comorbidity profiles and health outcomes, including distinct subtypes and progression patterns in colorectal cancer and systemic lupus erythematosus.
Identifiers
What Socratic 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 Socratic graph.