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

Quality Improvement (260) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Electronic Health Records (1068) Machine Learning (1501) Data Science (278) Clinical Informatics (1050) Cost-Effectiveness and Economics (259)

Published on in Vol 10, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37239, first published .
A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach

A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach

A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach

Authors of this article:

Suresh K Bhavnani1 Author Orcid Image ;   Weibin Zhang1 Author Orcid Image ;   Shyam Visweswaran2 Author Orcid Image ;   Mukaila Raji3 Author Orcid Image ;   Yong-Fang Kuo1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Zheng Q, Shen Q, Shu Z, Chang K, Zhong K, Yan Y, Ke J, Huang J, Su R, Xia J, Zhou X. Deep representation learning from electronic medical records identifies distinct symptom based subtypes and progression patterns for COVID-19 prognosis. International Journal of Medical Informatics 2024;191:105555 View

Books/Policy Documents

  1. Bhavnani S. Human Computer Interaction in Healthcare. View