Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.

Journal: BMC medical informatics and decision making
Published Date: Mar 10, 2022

Abstract

BACKGROUND: An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately.

Authors

  • Zhixu Hu
    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, People's Republic of China.
  • 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.
  • Liya Wang
    Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Minghui Shen
    Health Information Center of Sichuan Province, Chengdu, People's Republic of China.

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