Wang Min, Hu Zhao, Wang Ziyang, Chen Haoran, Xu Xiaowei, Zheng Si, Yao Yan, Li Jiao
Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China.
Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases, Fuwai Hospital, Beijing 100037, China.
Diagnostics (Basel). 2024 Oct 16;14(20):2291. doi: 10.3390/diagnostics14202291.
Ventricular tachycardia (VT) can broadly be categorised into ischemic heart disease, non-ischemic structural heart disease, and idiopathic VT. There are few studies related to the application of machine learning for the etiological diagnosis of VT, and the interpretable methods are still in the exploratory stage for clinical decision-making applications. The aim is to propose a machine learning model for the etiological diagnosis of VT. Interpretable results based on models are compared with expert knowledge, and interpretable evaluation protocols for clinical decision-making applications are developed. A total of 1305 VT patient data from 1 January 2013 to 1 September 2023 at the Arrhythmia Centre of Fuwai Hospital were included in the study. Clinical data collected during hospitalisation included demographics, medical history, vital signs, echocardiographic results, and laboratory test outcomes. The XGBoost model demonstrated the best performance in VT etiological diagnosis (precision, recall, and F1 were 88.4%, 88.5%, and 88.4%, respectively). A total of four interpretable machine learning methods applicable to clinical decision-making were evaluated in terms of visualisation, clinical usability, clinical applicability, and efficiency with expert knowledge interpretation. The XGBoost model demonstrated superior performance in the etiological diagnosis of VT, and SHAP and decision tree interpretable methods are more favoured by clinicians for decision-making.
室性心动过速（VT）大致可分为缺血性心脏病、非缺血性结构性心脏病和特发性室性心动过速。关于机器学习在室性心动过速病因诊断中的应用研究较少，且可解释方法在临床决策应用方面仍处于探索阶段。目的是提出一种用于室性心动过速病因诊断的机器学习模型。将基于模型的可解释结果与专家知识进行比较，并制定用于临床决策应用的可解释评估方案。本研究纳入了2013年1月1日至2023年9月1日期间阜外医院心律失常中心的1305例室性心动过速患者的数据。住院期间收集的临床数据包括人口统计学信息、病史、生命体征、超声心动图结果和实验室检查结果。XGBoost模型在室性心动过速病因诊断中表现出最佳性能（精确率、召回率和F1值分别为88.4%、88.5%和88.4%）。从可视化、临床可用性、临床适用性和专家知识解释效率方面，对总共四种适用于临床决策的可解释机器学习方法进行了评估。XGBoost模型在室性心动过速病因诊断中表现出卓越性能，而SHAP和决策树可解释方法在临床决策中更受临床医生青睐。
