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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38161, first published .
Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study

Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study

Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study

Lu Wang   1, 2 , PhD ;   Yilun Zhang   1 , MSc ;   Mark Chignell   1 , PhD ;   Baizun Shan   1 , MSc ;   Kathleen A Sheehan   3, 4 , MSc, MD, PhD ;   Fahad Razak   3, 5 , MPhil, MD ;   Amol Verma   3, 5 , MPhil, MD

1 Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada

2 Department of Computer Science, Texas State University, San Marcos, TX, United States

3 GEMINI - The General Medicine Inpatient Initiative, Unity Health Toronto, Toronto, ON, Canada

4 Department of Psychiatry, University of Toronto, Toronto, ON, Canada

5 Faculty of Medicine & Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

Corresponding Author:

  • Mark Chignell, PhD
  • Department of Mechanical & Industrial Engineering
  • University of Toronto
  • RM 8171A, Bahen Building
  • 40 St George Rd
  • Toronto, ON, M5S 2E4
  • Canada
  • Phone: 1 6473898951
  • Email: chignel@mie.utoronto.ca