School of Management, Xi'an Jiaotong University, Xi'an, 710049, China.
Department of System Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.
Sci Rep. 2019 Feb 25;9(1):2701. doi: 10.1038/s41598-019-39066-9.
Aortic dissection is one of the most clinical-challenging and life-threatening cardiovascular diseases associated with high morbidity and mortality. Aortic dissection requires fast diagnosis and timely therapy. Any delay or misdiagnosis can cause severe consequence to aortic dissection patients with even higher mortality. To better help physicians identify the potential dissection within the scope of all misdiagnosed patients, this paper describes a method which is developed with data mining methods for aortic dissection patient classification and prediction in the phase of early diagnosis. Various machine learning algorithms were used to build the models which were all trained and tested on the patient dataset with cross validation. Among them, Bayesian Network model achieved the best performance by predicting at a precision rate of 84.55% with Area Under the Curve (AUC) value of 0.857. On this basis, the Bayesian Network model can help physicians better with early diagnosis of aortic dissection in clinical practice. Beyond this study, more data from diverse regions and the internal pathology can be crucial to further build a universal model with broader predictive power.
主动脉夹层是一种极具临床挑战性和危及生命的心血管疾病，其发病率和死亡率均较高。主动脉夹层需要快速诊断和及时治疗。任何延误或误诊都会对主动脉夹层患者造成严重后果，甚至导致更高的死亡率。为了帮助医生在所有误诊患者的范围内识别潜在的夹层，本文描述了一种使用数据挖掘方法对主动脉夹层患者进行分类和早期诊断预测的方法。使用各种机器学习算法在具有交叉验证的患者数据集上构建模型并进行训练和测试。其中，贝叶斯网络模型通过预测准确率达到 84.55%，曲线下面积（AUC）值为 0.857，实现了最佳性能。在此基础上，贝叶斯网络模型可以帮助医生更好地进行临床实践中的主动脉夹层早期诊断。除了这项研究之外，来自不同地区和内部病理的更多数据对于进一步建立具有更广泛预测能力的通用模型至关重要。
