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

Machine Learning (2996) Tools, Programs and Algorithms (552) Decision Support for Health Professionals (2075) Electronic Health Records (1632) Advanced Data Analytics in eHealth (326) Ontologies, Classifications, and Coding (405) Nephrology/Renal Disorders (131)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68830, first published .
Doctor's hands typing on a laptop, wearing a white coat.

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 (2) 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

Conference Proceedings

  1. Das T, Ghosh S, Mishra M. 2026 International Conference on Recent Advancement in Electrical, Computer and Communication Technologies (IECCT). Convolutional Adversarial Autoencoder Network (CAAN)-based Automated Health Record Retrieval System Using Iris Images View