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

Machine Learning (1505) Intensive Care Unit (ICU) (114) Clinical Information and Decision Making (1438) Decision Support for Health Professionals (1211)

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21347, first published .
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Authors of this article:

Khalid Alghatani1 Author Orcid Image ;   Nariman Ammar2 Author Orcid Image ;   Abdelmounaam Rezgui3 Author Orcid Image ;   Arash Shaban-Nejad2 Author Orcid Image
Article Authors Cited by (44) Tweetations (3) Metrics

Khalid Alghatani   1 , PhD ;   Nariman Ammar   2 , PhD ;   Abdelmounaam Rezgui   3 , PhD ;   Arash Shaban-Nejad   2 , PhD

1 Department of Computer Science and Engineering, New Mexico Institute of Mining and Technology, Socorro, NM, United States

2 Oak Ridge National Laboratory Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, United States

3 School of Information Technology, Illinois State University, Normal, IL, United States

Corresponding Author:

  • Khalid Alghatani, PhD
  • Department of Computer Science and Engineering
  • New Mexico Institute of Mining and Technology
  • 801 Leroy Pl
  • Socorro, NM, 87801
  • United States
  • Phone: 1 5057204644
  • Email: khalid.alghatani@student.nmt.edu