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

Data Science (270) Stroke (163) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Clinical Information and Decision Making (30) Machine Learning (1465)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36477, first published .
A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study

A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study

A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study

Authors of this article:

Min Chen1 Author Orcid Image ;   Xuan Tan2 Author Orcid Image ;   Rema Padman3 Author Orcid Image
Article Authors Cited by (5) Tweetations (8) Metrics

Min Chen   1 , BA, MPP, PhD ;   Xuan Tan   2 , BSc, MSc, PhD ;   Rema Padman   3 , BTECH, MS, PhD

1 Department of Information Systems & Business Analytics, College of Business, Florida International University, Miami, FL, United States

2 Department of Information Systems and Analytics, Leavey School of Business, Santa Clara University, Santa Clara, CA, United States

3 The H John Heinz III College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, United States

Corresponding Author:

  • Rema Padman, BTECH, MS, PhD
  • The H John Heinz III College of Information Systems and Public Policy
  • Carnegie Mellon University
  • 4800 Forbes Avenue
  • Hamburg Hall 2101D
  • Pittsburgh, PA, 15213
  • United States
  • Phone: 1 412 268 2180
  • Email: rpadman@cmu.edu