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

Machine Learning (1501) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Depression and Mood Disorders; Suicide Prevention (1392) Perinatal Depression; Postpartum Depression; PPD (96)

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 (49) Tweetations (13) Metrics

Journals

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  23. Xu W, Sampson M. Prenatal and Childbirth Risk Factors of Postpartum Pain and Depression: A Machine Learning Approach. Maternal and Child Health Journal 2023;27(2):286 View
  24. Fischbein R, Cook H, Baughman K, Díaz S. Using machine learning to predict help-seeking among 2016–2018 Pregnancy Risk Assessment Monitoring System participants with postpartum depression symptoms. Women's Health 2022;18 View
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Books/Policy Documents

  1. Belciug S, Iliescu D. Pregnancy with Artificial Intelligence. View
  2. Raisa J, Kaiser M, Mahmud M. Brain Informatics. View
  3. Miranda E, Kumbangsila M, Aryuni M, Richard , Zakiyyah A, Sano A. Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics. View
  4. Priyanka , Goyal S, Bhatia R. Communication and Intelligent Systems. View