Document Type : Original Article


1 Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran

2 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran

3 Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran


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.


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