Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.

Journal: BMC medical informatics and decision making
Published Date: May 1, 2020

Abstract

BACKGROUND: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017.

Authors

  • Hang Qiu
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, Sichuan, P.R. China. qiuhang@uestc.edu.cn.
  • Lin Luo
    Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Ziqi Su
    Department of Statistics, Faculty of Science, University of British Columbia, Vancouver, Canada.
  • Li Zhou
    School of Education, China West Normal University, Nanchong, Sichuan, China.
  • Liya Wang
    Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Yucheng Chen
    Cardiology Division, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

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