Clinical data-based modeling of IVF live birth outcome and its application.

Journal: Reproductive biology and endocrinology : RB&E
PMID: 38978032

Abstract

BACKGROUND: The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, this study aims to establish classification models for predicting live birth outcome (LBO) with machine learning methods.

Authors

  • Liu Liu
    Department of Oral and Maxillofacial Radiology, School of Dentistry, Dental Science Research Institute, Chonnam National University, Gwangju, South Korea.
  • Hua Liang
    Qilu Hospital of Shandong University, Department of Nephrology, Jinan, Shandong, China.
  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Fujin Shen
    Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China. shenfj_rmh@outlook.com.
  • Jiao Chen
    Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine Nanjing 210028 China.
  • Liangfei Ao
    Wuhan Jinxin Gynecology and Obstetrics Hospital of Integrative Medicine, Wuhan, Hubei, China. aoliangfei@whu.edu.cn.

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