Oliveira Yuri, Rios Iêda, Araújo Paula, Macambira Alinne, Guimarães Marcos, Sales Lúcia, Rosa Júnior Marcos, Nicola André, Nakayama Mauro, Paschoalick Hermeto, Nascimento Francisco, Castillo-Salgado Carlos, Ferreira Vania Moraes, Carvalho Hervaldo
School of Medicine, University of Brasilia, Brasilia, Brazil.
School of Health Sciences, University of Brasilia, Brasilia, Brazil.
Front Artif Intell. 2024 Dec 18;7:1495074. doi: 10.3389/frai.2024.1495074. eCollection 2024.
In 2019, COVID-19 began one of the greatest public health challenges in history, reaching pandemic status the following year. Systems capable of predicting individuals at higher risk of progressing to severe forms of the disease could optimize the allocation and direction of resources. In this work, we evaluated the performance of different Machine Learning algorithms when predicting clinical outcomes of patients hospitalized with COVID-19, using clinical data from hospital admission alone. This data was collected during a prospective, multicenter cohort that followed patients with respiratory syndrome during the pandemic. We aimed to predict which patients would present mild cases of COVID-19 and which would develop severe cases. Severe cases were defined as those requiring access to the Intensive Care Unit, endotracheal intubation, or even progressing to death. The system achieved an accuracy of 80%, with Area Under Receiver Operating Characteristic Curve (AUC) of 91%, Positive Predictive Value of 87% and Negative Predictive Value of 82%. Considering that only data from hospital admission was used, and that this data came from low-cost clinical examination and laboratory testing, the low false positive rate and acceptable accuracy observed shows that it is feasible to implement prediction systems based on artificial intelligence as an effective triage method.
2019年，新型冠状病毒肺炎引发了历史上最严峻的公共卫生挑战之一，并于次年演变为全球大流行。能够预测个体发展为重症的风险的系统可以优化资源的分配和导向。在这项研究中，我们仅使用入院时的临床数据，评估了不同机器学习算法在预测新型冠状病毒肺炎住院患者临床结局方面的性能。这些数据来自一项前瞻性多中心队列研究，该研究在疫情期间对患有呼吸综合征的患者进行了随访。我们旨在预测哪些患者会出现轻症新型冠状病毒肺炎，哪些会发展为重症。重症病例定义为需要入住重症监护病房、进行气管插管甚至死亡的病例。该系统的准确率为80%，受试者工作特征曲线下面积（AUC）为91%，阳性预测值为87%，阴性预测值为82%。考虑到仅使用了入院时的数据，且这些数据来自低成本的临床检查和实验室检测，观察到的低假阳性率和可接受的准确率表明，将基于人工智能的预测系统作为一种有效的分诊方法是可行的。
