Extract critical factors affecting the length of hospital stay of pneumonia patient by data mining (case study: an Iranian hospital)

作者：

Highlights：

• Machine learning models are used to explore the important factors affecting the LOS of patients with pneumonia in hospitals.

• The LOS at hospital depends on patients’ discharge summary.

• The Bayesian boosting achieves 95.17% on overall accuracy, 74.13 ± 19.77% on weighted_mean_precision, and 74.93 ± 17.76% on weighted_mean_recall.

摘要

•Machine learning models are used to explore the important factors affecting the LOS of patients with pneumonia in hospitals.•The LOS at hospital depends on patients’ discharge summary.•The Bayesian boosting achieves 95.17% on overall accuracy, 74.13 ± 19.77% on weighted_mean_precision, and 74.93 ± 17.76% on weighted_mean_recall.

论文关键词：Length of stay (LOS),Medical data mining,Pneumonia,Patients,Ensemble methods

论文评审过程：Received 3 February 2017, Revised 21 June 2017, Accepted 28 June 2017, Available online 13 July 2017, Version of Record 17 November 2017.

论文官网地址：https://doi.org/10.1016/j.artmed.2017.06.010