Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning.

Journal: Journal of inflammation research
Published Date: Jan 1, 2025

Abstract

OBJECTIVE: The purpose of this study was to develop and validate machine learning models that can predict superaverage length of stay in hypercapnic-type respiratory failure and to compare the performance of each model. Furthermore, screen and select the optimal individualized risk assessment model. This model is capable of predicting in advance whether an inpatient's length of stay will exceed the average duration, thereby enhancing its clinical application and utility.

Authors

  • Bingqing Zuo
    Department of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, Jiangsu, 224006, People's Republic of China.
  • Lin Jin
    Third Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Kunming Medical University, Kuming, Yunnan, 650000, People's Republic of China.
  • Zhixiao Sun
    Department of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, Jiangsu, 224006, People's Republic of China.
  • Hang Hu
    Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA.
  • Yuan Yin
    Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, People's Republic of China.
  • Shuanying Yang
    Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, 710004, People's Republic of China.
  • Zhongxiang Liu
    Department of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, Jiangsu, 224006, People's Republic of China.

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