Implementation of a machine learning model in acute coronary syndrome and stroke risk assessment for patients with lower urinary tract symptoms.

Journal: Taiwanese journal of obstetrics & gynecology
PMID: 39004479

Abstract

OBJECTIVE: The global population is aging and the burden of lower urinary tract symptoms (LUTS) is expected to increase. According to the National Health Insurance Research Database, our previous studies have showed LUTS may predispose patients to cardiovascular disease. However, it is difficult to provide a personalized risk assessment in the context of "having acute coronary syndrome (ACS) and stroke." This study aimed to develop an artificial intelligence (AI)-based prediction model for patients with LUTS.

Authors

  • Tzu-Tsen Shen
    Division of Urogynecology, Department of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan.
  • Chung-Feng Liu
    Department of Medical Research, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
  • Ming-Ping Wu
    Division of Urogynecology, Department of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan; Department of Post-Baccalaureate Medicine, School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan. Electronic address: mpwu@mail.chimei.org.tw.

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