ArticleJAMIA open2021
Comparative effectiveness of medical concept embedding for feature engineering in phenotyping.
Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- KEEP: Integrating Medical Ontologies with Clinical Data for Robust Code Embeddings.Proceedings of machine learning research · 2025Article
- Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks.NPP - digital psychiatry and neuroscience · 2025Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network.Scientific data · 2023Article
- SymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes using Graph Clustering.Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing · 2023Article
- OARD: Open annotations for rare diseases and their phenotypes based on real-world data.American journal of human genetics · 2022Article
- Phenotyping in distributed data networks: selecting the right codes for the right patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.Patterns (New York, N.Y.) · 2021Article
- Severity Prediction for COVID-19 Patients via Recurrent Neural Networks.medRxiv : the preprint server for health sciences · 2021Article
- Severity Prediction for COVID-19 Patients via Recurrent Neural Networks.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
objectiveFeature engineering is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. We compared the effectiveness of MCEs learned from knowledge graphs and electronic healthcare records (EHR) data in retrieving relevant medical features for phenotyping tasks. MATERIALS AND
methodsWe implemented 5 embedding methods including node2vec, singular value decomposition (SVD), LINE, skip-gram, and GloVe with 2 data sources: (1) knowledge graphs obtained from the observational medical outcomes partnership (OMOP) common data model; and (2) patient-level data obtained from the OMOP compatible electronic health records (EHR) from Columbia University Irving Medical Center (CUIMC). We used phenotypes with their relevant concepts developed and validated by the electronic medical records and genomics (eMERGE) network to evaluate the performance of learned MCEs in retrieving phenotype-relevant concepts.
resultsAmong all MCEs, MCEs learned by using node2vec with knowledge graphs showed the best performance. Of MCEs based on knowledge graphs and EHR data, MCEs learned by using node2vec with knowledge graphs and MCEs learned by using GloVe with EHR data outperforms other MCEs, respectively.
conclusionMCE enables scalable feature engineering tasks, thereby facilitating phenotyping. Based on current phenotyping practices, MCEs learned by using knowledge graphs constructed by hierarchical relationships among medical concepts outperformed MCEs learned by using EHR data.
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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.