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

Machine Learning (1937) Advanced Data Analytics in eHealth (189) Big Data (303) Natural Language Processing (861) Secondary Use of Clinical Data for Research and Surveillance (449) Clinical Information and Decision Making (1792)

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 (2) Tweetations (2) Metrics

Journals

  1. Murali L, Gopakumar G, Viswanathan D, Nedungadi P. Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study. Journal of Biomedical Informatics 2023;143:104403 View
  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