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

Machine Learning (1400) Advanced Data Analytics in eHealth (112) Big Data (220) Natural Language Processing (650) Secondary Use of Clinical Data for Research and Surveillance (325) Clinical Information and Decision Making (1339)

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 (1) Tweetations (2) Metrics

Sajit Kumar   1 , MBA ;   Alicia Nanelia   2 , BSc ;   Ragunathan Mariappan   2 , MSc ;   Adithya Rajagopal   3 , BTech ;   Vaibhav Rajan   2 , PhD

1 Great Learning, Bengaluru, India

2 Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore

3 National Institute of Technology, Thiruchirappalli, India

Corresponding Author:

  • Vaibhav Rajan, PhD
  • Department of Information Systems and Analytics
  • National University of Singapore
  • 13 Computing Drive
  • Singapore, 117417
  • Singapore
  • Phone: 65 65166737
  • Email: vaibhav.rajan@nus.edu.sg