Derevitskii Ilia Vladislavovich, Mramorov Nikita Dmitrievich, Usoltsev Simon Dmitrievich, Kovalchuk Sergey V
National Center for Cognitive Research, ITMO University, 199034 Saint-Petersburg, Russia.
J Pers Med. 2022 Aug 17;12(8):1325. doi: 10.3390/jpm12081325.
The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.
本文的主要目标是开发一种预测重要临床指标的方法，该方法可用于改善治疗。我们使用数学预测建模算法，研究了住院治疗的新型冠状病毒肺炎（CP）的病程。所使用的算法包括动态和普通贝叶斯网络（OBN和DBN）、流行的机器学习算法、最先进的自动机器学习方法以及我们基于DBN和自动机器学习方法的新混合方法。预测目标包括治疗结果、住院时间、疾病严重程度指标的动态变化以及不同观察时间间隔内的处方药使用情况。模型通过专家知识、当前临床建议、先前研究和经典预测指标进行验证。最佳模型的特征如下：预测住院时间的平均绝对误差为3.6天（DBN加FEDOT自动机器学习框架），预测治疗结果的准确率为0.87（OBN）；预测处方药使用情况的F1分数为0.98（DBN）。此外，所提出方法的优势在于基于贝叶斯网络的可解释性，这在医学领域非常重要。在对其他医院的其他CP数据集进行验证后，所提出的模型可作为改善新型冠状病毒肺炎治疗的决策支持系统的一部分。另一个重要发现是新型冠状病毒肺炎与非新型冠状病毒肺炎之间存在显著差异。
