Unsupervised machine learning approach to interpret complex lower urinary tract symptoms and their impact on quality of life in adult women.

Journal: World journal of urology
Published Date:

Abstract

PURPOSE: To identify clinically meaningful clusters of lower urinary tract symptoms (LUTS) in adult women using an unsupervised machine learning approach and to examine their associations with patient-centered outcomes, including quality of life (QoL), willingness to pay (WTP) for treatment, and physician visits.

Authors

  • Kenji Omae
    Department of Innovative Research and Education for Clinicians and Trainees (DiRECT), Fukushima Medical University Hospital, Fukushima, Japan.
  • Noritoshi Sekido
    Epidemiological Survey Executive Committee, the Japanese Continence Society, Chiyoda, Tokyo, Japan.
  • Nobuhiro Haga
    1 Department of Urology, School of Medicine, Fukushima Medical University , Fukushima, Japan .
  • Yasue Kubota
    Department of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan.
  • Motoaki Saito
    Department of Obstetrics and Gynecology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan.
  • Ryuji Sakakibara
    Epidemiological Survey Executive Committee, the Japanese Continence Society, Chiyoda, Tokyo, Japan.
  • Mikako Yoshida
    Department of Women's Health Nursing & Midwifery, Tohoku University Graduate School of Medicine, Miyagi, Japan.
  • Takahiko Mitsui
    Department of Urology, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Chuo, Japan.
  • Naoya Masumori
    Department of Urology, Sapporo Medical University School of Medicine, Sapporo, Japan.
  • Satoru Takahashi
    Department of Endovascular Surgery, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan.