Development and application of a machine learning-based antenatal depression prediction model.

Journal: Journal of affective disorders
PMID: 39848469

Abstract

BACKGROUND: Antenatal depression (AND), occurring during pregnancy, is associated with severe outcomes. However, there is a lack of objective and universally applicable prediction methods for AND in clinical practice. We leveraged sociodemographic and pregnancy-related data to develop and validate a machine learning-based AND prediction model.

Authors

  • Chunfei Hu
    School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China; Department of Obstetrics and Gynecology, Shaoxing Maternal and Child Health Hospital, Shaoxing, Zhejiang, China.
  • Hongmei Lin
    Department of Obstetrics and Gynecology, Shaoxing Maternal and Child Health Hospital, Shaoxing, Zhejiang, China.
  • Yupin Xu
    School of Engineering and Informatics, University of Sussex, Falmer, Brighton, UK.
  • Xukun Fu
    Department of Medical Record, Shaoxing Maternal and Child Health Hospital, Shaoxing, Zhejiang, China.
  • Xiaojing Qiu
    Department of Nursing, Shengzhou Maternal and Child Health Hospital, Shengzhou, Zhejiang, China.
  • Siqian Hu
    Department of Obstetrics and Gynecology, Shaoxing Maternal and Child Health Hospital, Shaoxing, Zhejiang, China.
  • Tong Jin
  • Hualin Xu
    Department of Obstetrics and Gynecology, Shaoxing Maternal and Child Health Hospital, Shaoxing, Zhejiang, China. Electronic address: xuhualin@sxfby.com.
  • Qiong Luo
    State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong 999077, China.

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