Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study.

Journal: JMIR public health and surveillance
PMID:

Abstract

BACKGROUND: Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden.

Authors

  • Zhen Lu
    School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.
  • Binhua Dong
    Department of Gynecology, Laboratory of Gynecologic Oncology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
  • Hongning Cai
    Department of Gynecology, Maternal and Child Health Hospital of Hubei Province (Women and Children's Hospital of Hubei Province) Wuhan, Wuhan, China.
  • Tian Tian
    Laboratory Animal Center College of Animal Science Jilin University Changchun China.
  • Junfeng Wang
    Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China.
  • Leiwen Fu
    Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
  • Bingyi Wang
    School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
  • Weijie Zhang
    School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.
  • Shaomei Lin
    Department of Gynecology, Shunde Women's and Children's Hospital of Guangdong Medical University, Foshan, China.
  • Xunyuan Tuo
    Department of Gynecology, Gansu Provincial Maternity and Child-care Hospital, Lanzhou, China.
  • Juntao Wang
    Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia.
  • Tianjie Yang
    Department of Gynecology, Shenzhen Maternity & Child Healthcare Hospital, Shenzhen, China.
  • Xinxin Huang
    School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, P.R.China.
  • Zheng Zheng
    Department of Gynecology, Shenzhen Maternity & Child Healthcare Hospital, Shenzhen, China.
  • Huifeng Xue
    School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
  • Shuxia Xu
    Department of Pathology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
  • Siyang Liu
    Department of AIDS Research, State Key Laboratory of Pathogen and Biosafety, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China.
  • Pengming Sun
    Department of Gynecology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian, 350001, China. fmsun1975@fjmu.edu.cn.
  • Huachun Zou
    School of Public Health, Sun Yat-sen University, Shenzhen, China.