Mollura Maximiliano, Romano Stefano, Mantoan Giulio, Lehman Li-Wei, Barbieri Riccardo
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2768-2771. doi: 10.1109/EMBC44109.2020.9176276.
Septic Shock is a critical pathological state that affects patients entering the intensive care unit (ICU). Many studies have been directed to characterize and predict the onset of the septic shock, both in ICU and in the Emergency Department employing data extracted from the Electronic Health Records. Recently, machine learning algorithms have been successfully employed to help characterize septic shock in a more objective and automatic fashion. Only a few of these studies employ information contained in the continuously recorded vital signs such as electrocardiogram and arterial blood pressure. In particular, we have devised a novel feature estimation procedure able to consider instantaneous dynamics related to cardiovascular control. This work aims at developing a short-term prediction algorithm for identifying patients experiencing septic shock among a population of 100 septic patients extracted from the MIMIC-III clinical and waveform database. Among all the results obtained from several trained machine learning models, the best performance reached an AUC on the test set equal to 0.93 (Accuracy=0.85, Sensitivity=0.89 and Specificity=0.82).
脓毒性休克是一种影响进入重症监护病房（ICU）患者的关键病理状态。许多研究致力于通过从电子健康记录中提取的数据来表征和预测脓毒性休克在ICU和急诊科的发病情况。最近，机器学习算法已成功用于以更客观和自动的方式帮助表征脓毒性休克。这些研究中只有少数采用了连续记录的生命体征（如心电图和动脉血压）中包含的信息。特别是，我们设计了一种新颖的特征估计程序，能够考虑与心血管控制相关的瞬时动态。这项工作旨在开发一种短期预测算法，用于从MIMIC-III临床和波形数据库中提取的100名脓毒症患者群体中识别出正在经历脓毒性休克的患者。在从多个训练好的机器学习模型获得的所有结果中，测试集上的最佳性能达到了AUC等于0.93（准确率=0.85，灵敏度=0.89，特异性=0.82）。
