This paper is in the following e-collection/theme issue:

Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Depression and Mood Disorders; Suicide Prevention (1363) Perinatal Depression; Postpartum Depression; PPD (94)

Published on in Vol 8, No 4 (2020): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15516, first published .
Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study

Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study

Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study

Authors of this article:

Weina Zhang1 Author Orcid Image ;   Han Liu2 Author Orcid Image ;   Vincent Michael Bernard Silenzio3 Author Orcid Image ;   Peiyuan Qiu4 Author Orcid Image ;   Wenjie Gong1 Author Orcid Image
Article Authors Cited by (48) Tweetations (13) Metrics

Weina Zhang   1 , BSc ;   Han Liu   2 , MSc ;   Vincent Michael Bernard Silenzio   3 , MD, MPH ;   Peiyuan Qiu   4 , PhD ;   Wenjie Gong   1 , PhD

1 XiangYa School of Public Health, Central South University, Changsha, China

2 Sanofi Global Research and Design Operations Center, Chengdu, China

3 Urban-Global Public Health, Rutgers School of Public Health, Rutgers, The State University of New Jersey, Newark, NJ, United States

4 West China School of Public Health, Sichuan University, Chengdu, China

Corresponding Author:

  • Wenjie Gong, PhD
  • XiangYa School of Public Health
  • Central South University
  • 238 Shangmayuanling Lane Xiangya Road
  • Kaifu District
  • Changsha, 410005
  • China
  • Phone: 86 13607445252
  • Email: gongwenjie@csu.edu.cn