This paper is in the following e-collection/theme issue:

Machine Learning (2173) Tools, Programs and Algorithms (394) Electronic Health Records (1354) Decision Support for Health Professionals (1594) Advanced Data Analytics in eHealth (225) Nephrology/Renal Disorders (106) Ontologies, Classifications, and Coding (338)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68830, first published .
Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study

Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study

Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study

Authors of this article:

Deyi Li1 Author Orcid Image ;   Aditi Shukla2 Author Orcid Image ;   Sravani Chandaka3 Author Orcid Image ;   Bradley Taylor4 Author Orcid Image ;   Jie Xu1 Author Orcid Image ;   Mei Liu1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Sreenivasan M, Madhavendranath S, Chacko A. Temporal and comorbidity-aware representation of longitudinal patient trajectories from electronic health records. Biomedical Physics & Engineering Express 2026;12(1):015059 View