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

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, CA

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

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

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

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

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

Corresponding Author:

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