摘要
目的介绍一种基于住院患者心电图及临床特征开发的机器学习模型,用于诊断反射性晕厥。方法入选2018年6月20日至2022年5月11日于天津医科大学第二医院心脏科住院治疗的晕厥患者,经过临床评估和调查研究获得相关基线资料。确定了晕厥患者的15个特征,并进行特征排序。采用不同的机器学习方法构建反射性晕厥的诊断模型,如Logistic回归分析、感知机、支持向量机、决策树、随机森林和K最近邻算法等方法。结果最终入选410例患者,首次晕厥事件的年龄(64.5±14.6)岁,其中男236例(236/410,57.6%),65例患者确诊为反射性晕厥。纳入特征重要性排序结果位于前4位的特征构建模型,随机森林模型诊断反射性晕厥的性能最佳,曲线下面积为0.644,精确率(Precision)、召回率(Recall)和F1得分(F1 score)分别为0.794、0.849和0.791。结论人工智能算法能够识别反射性晕厥,可作为一种经济有效的筛查工具。
Objective To develop a machine learning model based on electrocardiogram and clinical characteristics of hospitalized patients for identifying reflex syncope.Methods Syncope patients hospitalized between June 20,2018 and May 11,2022 were included in Department of Cardiology,the Second Hospital of Tianjin Medical University.Standardized clinical variables were collected by evaluation.Fifteen features of syncope patients were identified,and features ranking was developed.Different machine learning methods were used to construct a prediction model for reflex syncope,such as Logistic regression,perceptron,support vector machines,decision tree,random forest and K-nearest neighbor.Results A total of 410 patients were enrolled,and 65 patients were diagnosed as reflex syncope.The average age of first onset was(64.5±14.6)years.There were 236 males(236/410,57.6%).The best prediction performance was obtained by using random forest with the top 4 features derived from feature importance ranking.The algorithm showed an area under the curve of 0.644 for diagnosis of cardiac syncope with a Precision,Recall and F1 score of 0.794,0.849 and 0.791,respectively.Conclusion Artificial intelligence algorithms can identify reflex syncope as a cost-effective and efficient screening tool.
作者
李歆慕
章德云
高欣怡
李秀莲
梁燕
刘文玲
洪申达
刘彤
Li Xinmu;Zhang Deyun;Gao Xinyi;Li Xiulian;Liang Yan;Liu Wenling;Hong Shenda;Liu Tong(Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease,Department of Cardiology,Tianjin Institute of Cardiology,The Second Hospital of Tianjin Medical University,Tianjin 300211,China;HeartVoice Medical Technology,Hefei 230000,China;Department of Cardiology,Peking University People′s Hospital,Beijing 100044,China;National Institute of Health Data Science at Peking University,Beijing 100191,China)
出处
《中华心律失常学杂志》
2022年第5期418-423,共6页
Chinese Journal of Cardiac Arrhythmias
基金
国家自然科学基金(81970270,62102008)。
关键词
晕厥
人工智能
诊断
心电图
临床特征
Syncope
Artificial intelligence
Diagnosis
Electrocardiogram
Clinical characteristics
