Machine-Learning-Based Predictive Model for Bothersome Stress Urinary Incontinence Among Parous Women in Southeastern China.

Journal: International urogynecology journal
PMID: 39585381

Abstract

INTRODUCTION AND HYPOTHESIS: Accurate identification of female populations at high risk for urinary incontinence (UI) and early intervention are potentially effective initiatives to reduce the prevalence of UI. We aimed to apply machine-learning techniques to establish, internally validate, and provide interpretable risk assessment tools.

Authors

  • Qi Wang
    Biotherapeutics Discovery Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Xiaoxiang Jiang
    Department of Gynecology, Fujian Maternity and Child Health Hospital, 18 Dao-Shan Street, Gu-Lou District, Fuzhou, 350000, PR China.
  • Xiaoyan Li
    Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China.
  • Yanzhen Que
    Department of Gynecology and Obstetrics, Shaxian General Hospital, Sanming, PR China.
  • Chaoqin Lin
    Department of Gynecology, Fujian Maternity and Child Health Hospital, 18 Dao-Shan Street, Gu-Lou District, Fuzhou, 350000, PR China. lcqfjsfy@126.com.

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