ArticlePatterns (New York, N.Y.)2021
Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- Exploring nationwide patterns of sleep problems from late adolescence to adulthood using machine learning.Science advances · 2025Article
- A self-supervised framework for laboratory data imputation in electronic health records.Communications medicine · 2025Article
- A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data.NPJ digital medicine · 2025Article
- A probabilistic approach for building disease phenotypes across electronic health records.BioData mining · 2025Article
- Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.PLOS digital health · 2025Article
- Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.medRxiv : the preprint server for health sciences · 2024Article
- Harnessing EHR data for health research.Nature medicine · 2024Review
- A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.Artificial intelligence in medicine · 2024Article
- Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York.International journal of medical informatics · 2024Article
- Automated HIV Case Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems.medRxiv : the preprint server for health sciences · 2023Article
- Cardiometabolic and renal phenotypes and transitions in the United States population.Nature cardiovascular research · 2023Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- Construction of Cohorts of Similar Patients From Automatic Extraction of Medical Concepts: Phenotype Extraction Study.JMIR medical informatics · 2022Article
- Visualization of medical concepts represented using word embeddings: a scoping review.BMC medical informatics and decision making · 2022Article
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Authors and funding
8 authors.
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Abstract
Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.
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