Using Machine Learning Models to Predict In- Hospital Mortality for ST-Elevation Myocardial Infarction Patients

摘要：

　　Acute myocardial infarction is a major cause of hospitalization and mortality in China, where ST-elevation myocardial infarction (STEMI) is more severe and has a higher mortality rate. Accurate and interpretable prediction of in-hospital mortality is critical for STEMI patient clinical decision making. In this study, we used interpretable machine learning approaches to build in-hospital mortality prediction models for STEMI patients from Chinese Acute Myocardial Infarction (CAMI) registry data. We first performed cohort construction and feature engineering on CAMI data to generate an available dataset and identify potential predictors. Then several supervised learning methods with good interpretability, including generalized linear models, decision tree models, and Bayes models, were applied to build prediction models. The experimental results show that our models achieve higher prediction performance (AUC = 0.80~0.85) than the previous in-hospital mortality prediction STEMI models and are also easily interpretable for clinical decision support.

关键词： Myocardial Infarction Hospital Mortality Machine Learning

作者: Xiang Li Haifeng Liu Jingang Yang Guotong Xie Meilin Xu Yuejin Yang

作者单位: IBM Research-China,Beijing,China Department of Cardiology,Fuwai Hospital,National Center for Cardiovascular Diseases,Beijing,China Pfizer Investment Co. Ltd.,Beijing,China

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 476-480

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）