Bottino Francesca, Tagliente Emanuela, Pasquini Luca, Napoli Alberto Di, Lucignani Martina, Figà-Talamanca Lorenzo, Napolitano Antonio
Medical Physics Department Bambino Gesù Children's Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy.
Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, 00165 Rome, Italy.
J Pers Med. 2021 Sep 7;11(9):893. doi: 10.3390/jpm11090893.
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.
自2019年冠状病毒病（COVID）首例病例报告以来，已经过去了一年多的时间，死亡人数仍在不断增加。尽量缩短资源分配和临床决策所需的时间，如分诊、通气模式的选择和重症监护病房的收治，非常重要。机器学习技术在预测COVID患者的预后方面正发挥着越来越重要的作用。特别是，基线机器学习技术在COVID死亡率预测中的应用正在迅速发展，因为死亡率预测模型可以快速有效地帮助对处于死亡风险中的COVID患者进行临床决策。最近的研究回顾了针对严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）诊断、严重程度、住院时间、重症监护病房收治或机械通气模式结果的预测模型；然而，文献中缺乏对使用机器学习方法预测COVID死亡率结果的系统评价。本综述研究了在COVID死亡率预测中实施机器学习（包括深度学习）方法的研究，试图展示现有的已发表文献，并对这些研究所取得的最佳结果提供可能的解释。该研究还讨论了当前研究的挑战性方面，并为未来的发展提供了建议。
