Obmann Dirk, Münch Philipp, Graf Bernhard, von Jouanne-Diedrich Holger, Zausig York A
Department of Anaesthesiology and Critical Care, Klinikum Aschaffenburg-Alzenau, Aschaffenburg, Germany.
Department of Anaesthesiology, University of Regensburg, Regensburg, Germany.
Sci Rep. 2025 May 6;15(1):15850. doi: 10.1038/s41598-025-00830-9.
Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex because they share certain symptoms. Using the Medical Information Mart for Intensive Care (MIMIC)-III database and artificial intelligence (AI), we aimed to increase diagnostic precision, focusing on Bayesian network classifiers (BNCs) and comparing them with other AI methods. Data from 5970 adults, including 950 patients with cardiogenic shock, 1946 patients with septic shock, and 3074 patients with sepsis, were extracted for this study. Of the original 51 variables included in the data records, 12 were selected for constructing the predictive model. The data were divided into training and validation sets at an 80:20 ratio, and the performance of the BNCs was evaluated and compared with that of other AI models, such as the one rule classifier (OneR), classification and regression tree (CART), and an artificial neural network (ANN), in terms of accuracy, sensitivity, specificity, precision, and F1-score. The BNCs exhibited an accuracy of 87.6% to 91.5%. The CART model demonstrated a notable 91.6% accuracy when only three decision levels were used, whereas the intricate ANN model reached 90.5% accuracy. Both the BNCs and the CART model allowed clear interpretation of the predictions. BNCs have the potential to be valuable tools in diagnostic tasks, with an accuracy, sensitivity, and precision comparable, in some cases, to those of ANNs while demonstrating superior interpretability.
脓毒症、脓毒性休克和心源性休克是危及生命的病症，死亡率很高，但由于它们有某些共同症状，因此难以区分。我们利用重症监护医学信息集市（MIMIC）-III数据库和人工智能（AI），旨在提高诊断精度，重点关注贝叶斯网络分类器（BNC）并将其与其他AI方法进行比较。本研究提取了5970名成年人的数据，其中包括950名心源性休克患者、1946名脓毒性休克患者和3074名脓毒症患者。在数据记录中最初包含的51个变量中，选择了12个用于构建预测模型。数据按80:20的比例分为训练集和验证集，并从准确性、敏感性、特异性、精确性和F1分数方面评估了BNC的性能，并与其他AI模型（如单规则分类器（OneR）、分类与回归树（CART）和人工神经网络（ANN））的性能进行了比较。BNC的准确率为87.6%至91.5%。当仅使用三个决策级别时，CART模型的准确率达到了显著的91.6%，而复杂的ANN模型的准确率为90.5%。BNC和CART模型都能对预测结果进行清晰的解释。BNC有潜力成为诊断任务中的有价值工具，其准确性、敏感性和精确性在某些情况下与ANN相当，同时具有更好的可解释性。
