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

Alghatani, Khalid; Ammar, Nariman; Rezgui, Abdelmounaam; Shaban-Nejad, Arash

Alghatani, Khalid; Ammar, Nariman; Rezgui, Abdelmounaam; Shaban-Nejad, Arash.

Afiliación

Alghatani K; Department of Computer Science and Engineering, New Mexico Institute of Mining and Technology, Socorro, NM, United States.
Ammar N; 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.
Rezgui A; School of Information Technology, Illinois State University, Normal, IL, United States.
Shaban-Nejad A; 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.

JMIR Med Inform ; 9(5): e21347, 2021 May 05.

Article en En | MEDLINE | ID: mdl-33949961

Palabras clave

ICU patient monitoring; clinical intelligence; intensive care unit (ICU); machine learning; predictive model; vital signs measurements

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: JMIR Med Inform Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

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