COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal

Francesca Bottino, Emanuela Tagliente, Luca Pasquini, Alberto Di Napoli, Martina Lucignani, Lorenzo Figà-Talamanca, Antonio Napolitano

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish
JournalJournal of Personalized Medicine
Issue number9
Publication statusPublished - Sept 7 2021


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