Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling.

COVID-19 artificial intelligence length of stay predictive analytics risk of death

Journal

Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047

Informations de publication

Date de publication:
2021
Historique:
received: 07 08 2020
accepted: 10 03 2021
entrez: 21 5 2021
pubmed: 22 5 2021
medline: 22 5 2021
Statut: epublish

Résumé

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination

Identifiants

pubmed: 34017839
doi: 10.3389/fmed.2021.592336
pmc: PMC8129500
doi:

Types de publication

Journal Article

Langues

eng

Pagination

592336

Informations de copyright

Copyright © 2021 Mahboub, Bataineh, Alshraideh, Hamoudi, Salameh and Shamayleh.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Bassam Mahboub (B)

Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.

Mohammad T Al Bataineh (MTA)

Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.
Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.

Hussam Alshraideh (H)

Industrial Engineering Department, American University of Sharjah, Sharjah, United Arab Emirates.
Industrial Engineering Department, Jordan University of Science and Technology, Irbid, Jordan.

Rifat Hamoudi (R)

Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.
Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.
Division of Surgery and Interventional Science, University College London, London, United Kingdom.

Laila Salameh (L)

Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.

Abdulrahim Shamayleh (A)

Industrial Engineering Department, American University of Sharjah, Sharjah, United Arab Emirates.

Classifications MeSH