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

Machine Learning (1465) Ontologies, Classifications, and Coding (239) Decision Support for Health Professionals (1185) Clinical Informatics (1022) Artificial Intelligence (1371) Secondary Use of Clinical Data for Research and Surveillance (366) Data Science (270) Coronary Heart Disease (81) Tools, Programs and Algorithms (266)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52896, first published .
Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study

Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study

Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study

Authors of this article:

Peyman Ghasemi1, 2 Author Orcid Image ;   Joon Lee1, 3, 4, 5 Author Orcid Image
Article Authors Cited by Tweetations (2) Metrics

Peyman Ghasemi   1, 2 , MSc ;   Joon Lee   1, 3, 4, 5 , PhD

1 Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

2 Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada

3 Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

4 Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

5 Department of Preventive Medicine, School of Medicine, Kyung Hee University, Seoul, Republic of Korea

Corresponding Author:

  • Joon Lee, PhD
  • Data Intelligence for Health Lab
  • Cumming School of Medicine
  • University of Calgary
  • 3280 Hospital Drive NW
  • Calgary, AB, T2N 4Z6
  • Canada
  • Phone: 1 403 220 2968
  • Email: joon.lee@ucalgary.ca