Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data.

Abstract

Authors

• Ki Jin Ryu
  Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
• Kyong Wook Yi
  Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
• Yong Jin Kim
  Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea. Electronic address: yjgs1997@gmail.com.
• Jung Ho Shin
  Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
• Jun Young Hur
  Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
• Tak Kim
  Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
• Jong Bae Seo
  Department of Biosciences, Mokpo National University, Muan, Korea.
• Kwang Sig Lee
  Center for Artificial Intelligence, Korea University College of Medicine, Seoul, Korea.
• Hyuntae Park
  Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea. cyberpelvis@gmail.com.

Keywords

External Resources