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

Journal: Studies in health technology and informatics
Published Date:

Abstract

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.

Authors

  • Zhi Qiao
    IBM Research Lab - China.
  • Ning Sun
    State Key Laboratory of Chirosciences and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Eryu Xia
    IBM Research - China, Beijing, China.
  • Shiwan Zhao
    IBM Research China, Beijing, China.
  • Yong Qin
    IBM Research, Beijing, China.