A preliminary prediction model of pediatric Mycoplasma pneumoniae pneumonia based on routine blood parameters by using machine learning method.

Journal: BMC infectious diseases
PMID: 39026207

Abstract

BACKGROUND: The prevalence and severity of pediatric Mycoplasma pneumoniae pneumonia (MPP) poses a significant threat to the health and lives of children. In this study, we aim to systematically evaluate the value of routine blood parameters in predicting MPP and develop a robust and generalizable ensemble artificial intelligence (AI) model to assist in identifying patients with MPP.

Authors

  • Xuelian Peng
    Department of Laboratory Medicine, The Affiliated Dazu's Hospital of Chongqing Medical University, Chongqing, 402360, China.
  • Yulong Liu
    Department of Laboratory Medicine, The Affiliated Dazu's Hospital of Chongqing Medical University, Chongqing, 402360, China.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Chunyan Yang
    Research Institute of Extenics and Innovation Method, Guangdong University of Technology, Guangzhou, 510006, China.
  • Jian Dong
  • Chen Yong
    Department of Laboratory Medicine, The Affiliated Dazu's Hospital of Chongqing Medical University, Chongqing, 402360, China. 593489766@qq.com.
  • Baoru Han
    Medical Data Science Academy, College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China. baoruhan@cqmu.edu.cn.
  • Jin Li
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.

Keywords

External Resources

Popular Topics
Recent Journals