ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2018
Automated disease cohort selection using word embeddings from Electronic Health Records.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
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- A scalable framework for benchmark embedding models in semantic health-care tasks.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.Journal of biomedical informatics · 2025Article
- A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2025Article
- Mapping artificial intelligence adoption in hepatology practice and research: challenges and opportunities in MENA region.Frontiers in medicine · 2025Article
- A flexible symbolic regression method for constructing interpretable clinical prediction models.NPJ digital medicine · 2023Article
- Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration.BMC medical informatics and decision making · 2023Article
- Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study.Journal of medical Internet research · 2022Article
- Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.Patterns (New York, N.Y.) · 2021Article
- The role of machine learning in clinical research: transforming the future of evidence generation.Trials · 2021Review
- Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study.JMIR medical informatics · 2021Article
- A high-throughput phenotyping algorithm is portable from adult to pediatric populations.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Leveraging graph-based hierarchical medical entity embedding for healthcare applications.Scientific reports · 2021Article
- Article
- Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task.JAMIA open · 2020Article
- Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems.Journal of speech, language, and hearing research : JSLHR · 2020Article
- Identifying Acute Low Back Pain Episodes in Primary Care Practice From Clinical Notes: Observational Study.JMIR medical informatics · 2020Article
- Deep representation learning of electronic health records to unlock patient stratification at scale.NPJ digital medicine · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Accurate and robust cohort definition is critical to biomedical discovery using Electronic Health Records (EHR). Similar to prospective study designs, high quality EHR-based research requires rigorous selection criteria to designate case/control status particular to each disease. Electronic phenotyping algorithms, which are manually built and validated per disease, have been successful in filling this need. However, these approaches are time-consuming, leading to only a relatively small amount of algorithms for diseases developed. Methodologies that automatically learn features from EHRs have been used for cohort selection as well. To date, however, there has been no systematic analysis of how these methods perform against current gold standards. Accordingly, this paper compares the performance of a state-of-the-art automated feature learning method to extracting research-grade cohorts for five diseases against their established electronic phenotyping algorithms. In particular, we use word2vec to create unsupervised embeddings of the phenotype space within an EHR system. Using medical concepts as a query, we then rank patients by their proximity in the embedding space and automatically extract putative disease cohorts via a distance threshold. Experimental evaluation shows promising results with average F-score of 0.57 and AUC-ROC of 0.98. However, we noticed that results varied considerably between diseases, thus necessitating further investigation and/or phenotype-specific refinement of the approach before being readily deployed across all diseases.
Indexed as
Identifiers
29218877PMC5788312What 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.