Using Machine Learning Approaches for Emergency Room Visit Prediction Based on Electronic Health Record Data

被引：23

作者：

Qiao, Zhi ^{[1
]}

Sun, Ning ^{[1
]}

Li, Xiang ^{[1
]}

Xia, Eryu ^{[1
]}

Zhao, Shiwan ^{[1
]}

Qin, Yong ^{[1
]}

机构：

[1] IBM Res Lab China, Beijing, Peoples R China

来源：

BUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH | 2018年 / 247卷

关键词：

ER visit prediction; EHR; Machine Learning;

D O I：

10.3233/978-1-61499-852-5-111

中图分类号：

R-058 [];

学科分类号：

摘要：

Emergency room(ER) visit prediction, especially whether visit ER or not and ER visit count, is crucial for hospitals to reasonably adapt resource allocation and for patients to know future health state. Some existing studies have explored to use machine learning methods especially kinds of general linear model to settle down the task. But, in the clinical problems, there exist complex correlation between targets and features. Generally, liner model is difficult to model complex correlation to make better prediction. Hence, in this paper, we propose to use two non-linear models to settle the problem, which are XGBoost and Recurrent Neural Network. Experimental results show both methods have better performance.

引用

页码：111 / 115

页数：5