A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.

Journal: Respiratory research
PMID: 38720331

Abstract

BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the therapeutic outcome of preterm infants. Hence, early prevention and timely diagnosis prior to pathological change is the key to reducing morbidity and improving prognosis. Our primary objective is to utilize machine learning techniques to build predictive models that could accurately identify BPD infants at risk of developing PH.

Authors

  • Dan Wang
    Guangdong Pharmaceutical University Guangzhou Guangdong China.
  • Shuwei Huang
    School of Software, Tsinghua University, Beijing, China.
  • Jingke Cao
    Newborn Intensive Care Unit, Faculty of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beiing, China.
  • Zhichun Feng
    Department of Urology, Bayi Children's Hospital, Affiliated to The Seventh Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China.
  • Qiannan Jiang
    School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, China.
  • Wanxian Zhang
    Department of Neonatology, Tianjin Central Hospital of Gynecology Obstetrics, Tianjin, China.
  • Jia Chen
    Department of Oncology Internal Medicine, Nantong Tumor Hospital, Affiliated Tumor Hospital of Nantong University, Nantong, China.
  • Shelby Kutty
    Department of Pediatrics, at Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA.
  • Changgen Liu
    Newborn Intensive Care Unit, Faculty of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beiing, China.
  • Wenyu Liao
    Department of Statistics and Data Science, BNU-HKBU United International College, Zhuhai, China.
  • Le Zhang
    State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; College of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Science and Technology on Particle Materials, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 361021, China.
  • Guli Zhu
    Department of Statistics and Data Science, BNU-HKBU United International College, Zhuhai, China.
  • Wenhao Guo
    Department of Statistics and Data Science, BNU-HKBU United International College, Zhuhai, China.
  • Jie Yang
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Lin Liu
    Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University; Shanghai Artificial Intelligence Laboratory.
  • Jingwei Yang
    School of Mechatronic Engineering and Automation, Foshan University, Foshan, Guangdong, China.
  • Qiuping Li
    Newborn Intensive Care Unit, Faculty of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beiing, China. zhjhospital@163.com.

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