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

Clinical Information and Decision Making (1407) Machine Learning (1465) Intensive Care Unit (ICU) (112)

Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20268, first published .
Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Authors of this article:

Adrienne Kline1, 2, 3 Author Orcid Image ;   Theresa Kline4 Author Orcid Image ;   Zahra Shakeri Hossein Abad3, 5 Author Orcid Image ;   Joon Lee3, 5, 6 Author Orcid Image
Article Authors Cited by (6) Tweetations (8) Metrics

Adrienne Kline   1, 2, 3 , PhD ;   Theresa Kline   4 , MSc, PhD ;   Zahra Shakeri Hossein Abad   3, 5 , MSc, PhD ;   Joon Lee   3, 5, 6 , PhD

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

2 Undergraduate Medical Education, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

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

4 Department of Psychology, University of Calgary, Calgary, AB, Canada

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

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

Corresponding Author:

  • Adrienne Kline, PhD
  • Department of Biomedical Engineering
  • University of Calgary
  • 2500 University Drive NW
  • Calgary, AB
  • Canada
  • Phone: 1 5875831725
  • Email: askline1@gmail.com