Abstract
More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.
Highlights
More than a year has passed since the report of the first case of coronavirus disease2019 (COVID), and many deaths continue to occur
Admission, mechanical ventilation modality outcomes [8,9,10,11,12], highlighting pitfalls of the machine and deep learning methods based on imaging data [13]; systematic reviews focused on prediction of coronavirus disease 2019 (COVID) mortality outcome with machine learning (ML) methods, including
This systematic review considers the state of the art in ML and deep learning (DL) as applied to COVID mortality prediction
Summary
More than a year has passed since the report of the first case of coronavirus disease2019 (COVID), and many deaths continue to occur. Due to the rapid spread of the virus have required an improvement of patient management throughout the healthcare system In this context, it is important to minimize the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modality, admission to the intensive care unit. Baseline machine learning (ML) and deep learning (DL) techniques are widely accepted thanks to their ability to obtain information from the input data without “a priori” definitions [2] These approaches can be efficiently tested in healthcare applications such as diagnosis of diseases, analysis of medical images, collection of big data, research and clinical trials, management of smart health records, prediction of outbreaks [3]. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis and severity, length of hospital stay, intensive care unit (ICU)
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