Cluster Analysis of Low-Dimensional Medical Concept Representations from Electronic Health Records-Reference-Cited by-同舟云学术

Cluster Analysis of Low-Dimensional Medical Concept Representations from Electronic Health Records

Published:2022 Issue: Volume: Page:313-324
ISSN:0302-9743
Container-title:Health Information Science
language:
Short-container-title:

Author:

Jaume-Santero Fernando^ORCID,Zhang Boya^ORCID,Proios Dimitrios^ORCID,Yazdani Anthony^ORCID,Gouareb Racha^ORCID,Bjelogrlic Mina^ORCID,Teodoro Douglas^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-20627-6_29

Reference20 articles.

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3. Flamholz, Z.N., Crane-Droesch, A., Ungar, L.H., Weissman, G.E.: Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information. J. Biomed. Inform. 125, 103971 (2022). https://doi.org/10.1016/j.jbi.2021.103971. https://www.sciencedirect.com/science/article/pii/S1532046421003002

4. Glynn, E.F., Hoffman, M.A.: Heterogeneity introduced by EHR system implementation in a de-identified data resource from 100 non-affiliated organizations. JAMIA Open 2(4), 554–561 (2019). https://doi.org/10.1093/jamiaopen/ooz035. https://pubmed.ncbi.nlm.nih.gov/32025653

5. Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a K-means clustering algorithm. J. R. Stat. Soc. C: Appl. Stat. 28(1), 100–108 (1979). https://doi.org/10.2307/2346830. Full publication date 1979

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