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

Machine Learning (2235) Advanced Data Analytics in eHealth (237) Big Data (318) Natural Language Processing (956) Secondary Use of Clinical Data for Research and Surveillance (493) Clinical Information and Decision Making (2073)

Published on in Vol 10, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28842, first published .
Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study

Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study

Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study

Authors of this article:

Sajit Kumar1 Author Orcid Image ;   Alicia Nanelia2 Author Orcid Image ;   Ragunathan Mariappan2 Author Orcid Image ;   Adithya Rajagopal3 Author Orcid Image ;   Vaibhav Rajan2 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

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  2. Zheng Y, Bensahla A, Bjelogrlic M, Zaghir J, Turbe H, Bednarczyk L, Gaudet-Blavignac C, Ehrsam J, Marchand-Maillet S, Lovis C. A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data. npj Digital Medicine 2025;8(1) View
  3. Yadalam P, Sharma S, Ardila C. Knowledge-Aware Graph Neural Networks for TRPV1 Drug-Gene Associations in Periodontal Pain. Odovtos - International Journal of Dental Sciences 2025;28(1):136 View
  4. Guo L, Arciniegas S, Yan A, Fries J, Tomlinson G, Sung L. Systematic review of foundation models for structured electronic health records. Journal of the American Medical Informatics Association 2026 View