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

Data Science (305) Stroke (197) Clinical Information and Decision Making (1646) Decision Support for Health Professionals (1337) Machine Learning (1730)

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

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  2. Aksoy Ö, Ayvaci M, Cezar A, Raghunathan S. When Systemic Biases Taint Algorithms: A Path to More Equitable Access in Healthcare. SSRN Electronic Journal 2024 View
  3. Salman O, Abdul Latiff N, Syed Arifin S, Salman O. Early Triage Prediction for Outpatient Care Based on Heterogeneous Medical Data Utilizing Machine Learning. Pertanika Journal of Science and Technology 2024;32(5):2343 View
  4. Chen X, Zhang S. Development, assessment and validation of a novel nomogram model for predicting stroke mimics in stroke center：A single-center observational study. Heliyon 2024;10(19):e38602 View
  5. Chakraborty P, Bandyopadhyay A, Sahu P, Burman A, Mallik S, Alsubaie N, Abbas M, Alqahtani M, Soufiene B. Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing. BMC Bioinformatics 2024;25(1) View