Major clinical feature summary statistics.

A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.肺栓塞（pulmonary embolism, PE）误诊可引发残疾乃至死亡等严重后果。在临床实践中精准识别PE的关键临床特征至关重要，此举可及时发现无症状的潜在PE患者，并避免将出现呼吸困难或胸痛等症状的PE患者误诊为哮喘急性发作。然而，由于诸多因素以复杂方式影响PE发生风险（例如各因素间的交互作用），可靠识别此类关键特征颇具挑战。为解决这一难题，我们提出了一种结合深度神经网络（deep neural network, DNN）模型与基于排列的特征重要性检验（permutation-based feature importance test, PermFIT）流程的有效框架，即PermFIT-DNN。我们将该框架应用于哮喘急性发作患者的PE研究数据分析。分析结果显示，PermFIT-DNN框架可稳健识别用于分类PE状态的关键特征，所识别的重要特征还可辅助精准预测PE发病风险。