When we face patients arriving to a hospital suffering from the effects of some illness, one of the main problems we can encounter is evaluating whether or not said patients are going to require intensive care in the near future. This intensive care requires allotting valuable and scarce resources, and knowing beforehand the severity of a patients illness can improve both its treatment and the organization of resources. We illustrate this issue in a dataset consistent of Spanish COVID-19 patients from the sixth epidemic wave where we label patients as critical when they either had to enter the intensive care unit or passed away. We then combine the use of dynamic Bayesian networks, to forecast the vital signs and the blood analysis results of patients over the next 40 hours, and neural networks, to evaluate the severity of a patients disease in that interval of time. Our empirical results show that the transposition of the current state of a patient to future values with the DBN for its subsequent use in classification obtains better the accuracy and g-mean score than a direct application with a classifier.

翻译：当患者因疾病影响入院时，我们面临的主要问题之一是评估其在近期是否需要重症监护。重症监护需要分配宝贵且稀缺的资源，而提前了解患者病情的严重程度既能改善治疗，又能优化资源配置。我们基于第六波疫情中西班牙新冠肺炎患者数据集验证了这一问题，将患者需进入重症监护室或死亡的情况标记为危重状态。我们结合动态贝叶斯网络（预测患者未来40小时生命体征与血液分析结果）与神经网络（评估该时间段内患者病情严重程度）进行联合分析。实证结果表明，通过动态贝叶斯网络将患者当前状态映射为未来值并进行分类，其准确率和几何平均得分均优于直接使用分类器的应用方式。