Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks

Sat Jul 20 22:55:22 2024

(2023) Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks. Journal of Education and Health Promotion. p. 8. ISSN 2277-9531

Full text not available from this repository.

Official URL: <Go to ISI>://WOS:000935149800016


BACKGROUND: Accurately predicting the intubation risk in COVID-19 patients at the admission time is critical to optimal use of limited hospital resources, providing customized and evidence-based treatments, and improving the quality of delivered medical care services. This study aimed to design a statistical algorithm to select the best features influencing intubation prediction in coronavirus disease 2019 (COVID-19) hospitalized patients. Then, using selected features, multiple artificial neural network (ANN) configurations were developed to predict intubation risk.MATERIAL AND METHODS: In this retrospective single-center study, a dataset containing 482 COVID-19 patients who were hospitalized between February 9, 2020 and July 20, 2021 was used. First, the Phi correlation coefficient method was performed for selecting the most important features affecting COVID-19 patients' intubation. Then, the different configurations of ANN were developed. Finally, the performance of ANN configurations was assessed using several evaluation metrics, and the best structure was determined for predicting intubation requirements among hospitalized COVID-19 patients.RESULTS: The ANN models were developed based on 18 validated features. The results indicated that the best performance belongs to the 18-20-1 ANN configuration with positive predictive value (PPV) = 0.907, negative predictive value (NPV) = 0.941, sensitivity = 0.898, specificity = 0.951, and area under curve (AUC) = 0.906.CONCLUSIONS: The results demonstrate the effectiveness of the ANN models for timely and reliable prediction of intubation risk in COVID-19 hospitalized patients. Our models can inform clinicians and those involved in policymaking and decision making for prioritizing restricted mechanical ventilation and other related resources for critically COVID-19 patients.

Item Type: Article
Shanbezadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: Artificial intelligence coronavirus COVID-19 data mining intubation machine learning neural networks mortality prediction Education & Educational Research Public, Environmental & Occupational Health
Page Range: p. 8
Journal or Publication Title: Journal of Education and Health Promotion
Journal Index: ISI
Volume: 12
Number: 1
Identification Number:₂₀₂₂
ISSN: 2277-9531
Depositing User: مهندس مهدی شریفی

Actions (login required)

View Item View Item