ArticleBMC medical informatics and decision making2018
EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- Predicting 30-Days Hospital Readmission for Patients with Heart Failure Using Electronic Health Record Embeddings: Comparative Evaluation.JMIR medical informatics · 2025Article
- ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.Journal of biomedical informatics · 2025Article
- Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysis.Research square · 2024Article
- An explainable long short-term memory network for surgical site infection identification.Surgery · 2024Article
- Improving Diagnostics with Deep Forest Applied to Electronic Health Records.Sensors (Basel, Switzerland) · 2023Article
- Dynamic prediction of work status for workers with occupational injuries: assessing the value of longitudinal observations.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- MedCV: An Interactive Visualization System for Patient Cohort Identification from Medical Claim Data.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2022Article
- 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
- Improving medical term embeddings using UMLS Metathesaurus.BMC medical informatics and decision making · 2022Article
- Generalized and transferable patient language representation for phenotyping with limited data.Journal of biomedical informatics · 2021Article
- Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation.JMIR medical informatics · 2021Article
- Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.Digestive diseases and sciences · 2021Review
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- Medical Concept Representation Learning from Multi-source Data.IJCAI : proceedings of the conference · 2019Article
- Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThere has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive modeling of EHR data to gain insights about the patterns of care and health outcomes. EHRs contain structured data such as diagnostic codes and laboratory tests, as well as unstructured free text data in form of clinical notes, which provide more detail about condition and treatment of patients.
methodsIn this work, we propose a method that jointly learns vector representations of medical concepts and words. This is achieved by a novel learning scheme based on the word2vec model. Our model learns those relationships by integrating clinical notes and sets of accompanying medical codes and by defining joint contexts for each observed word and medical code.
resultsIn our experiments, we learned joint representations using MIMIC-III data. Using the learned representations of words and medical codes, we evaluated phenotypes for 6 diseases discovered by our and baseline method. The experimental results show that for each of the 6 diseases our method finds highly relevant words. We also show that our representations can be very useful when predicting the reason for the next visit.
conclusionsThe jointly learned representations of medical concepts and words capture not only similarity between codes or words themselves, but also similarity between codes and words. They can be used to extract phenotypes of different diseases. The representations learned by the joint model are also useful for construction of patient features.
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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.