Background: In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems. Objective: This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal. Methods: A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient’s cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization. Results: For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision. Conclusions: The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end. Trial Registration:

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中文翻译：

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葡萄牙 COVID-19 住院、重症监护病房入院和呼吸辅助的可预测性：纵向队列研究


背景：面对当前的 COVID-19 大流行，及时预测感染者即将到来的医疗需求可以在必要时提供更好更快的护理，并在医疗保健系统内做出管理决策。目的：这项工作旨在预测葡萄牙 SARS-CoV-2 感染检测呈阳性的个体的医疗需求（住院、重症监护病房入院和呼吸辅助）和生存能力。方法：使用 2020 年 38,545 名感染者的回顾性队列。在患者周期的各个阶段，即测试（住院前）、住院后和重症监护期间，使用最先进的机器学习方法来预测医疗需求。对最先进的预测因子进行了彻底优化，以评估使用人口统计和合并症变量以及与症状出现、检测和住院相关的日期来预测医疗需求和感染结果的能力。结果：对于目标队列，75% 的住院需求可以在检测 SARS-CoV-2 感染时确定。超过 60% 的呼吸需求可以在住院时确定。两个预测的精度均为 >50%。结论：所进行的研究确定了所提出的预测模型的相关性，作为支持葡萄牙人群医疗决策（包括监测和院内护理决策）的良好候选者。为此还提供了临床决策支持系统。试用注册：


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