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

Machine Learning (1401) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147) Depression and Mood Disorders; Suicide Prevention (1290) Perinatal Depression; Postpartum Depression; PPD (88)

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 (45) Tweetations (13) Metrics

Journals

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  18. Li X, Ono C, Warita N, Shoji T, Nakagawa T, Usukura H, Yu Z, Takahashi Y, Ichiji K, Sugita N, Kobayashi N, Kikuchi S, Kunii Y, Murakami K, Ishikuro M, Obara T, Nakamura T, Nagami F, Takai T, Ogishima S, Sugawara J, Hoshiai T, Saito M, Tamiya G, Fuse N, Kuriyama S, Yamamoto M, Yaegashi N, Homma N, Tomita H. Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women. Frontiers in Psychiatry 2022;12 View
  19. Yang S, Yang S, Duan K, Tang Y, Ping A, Bai Z, Gao K, Shen Y, Chen M, Yu R, Wang S. The development and application of a prediction model for postpartum depression: optimizing risk assessment and prevention in the clinic. Journal of Affective Disorders 2022;296:434 View
  20. Chen T, Chu H, Tai Y, Yang S. Performances of depression detection through deep learning-based natural language processing to mandarin chinese medical records: Comparison between civilian and military populations. Taiwanese Journal of Psychiatry 2022;36(1):32 View
  21. Novick A, Kwitowski M, Dempsey J, Cooke D, Dempsey A. Technology-Based Approaches for Supporting Perinatal Mental Health. Current Psychiatry Reports 2022;24(9):419 View
  22. Matsuo S, Ushida T, Emoto R, Moriyama Y, Iitani Y, Nakamura N, Imai K, Nakano‐Kobayashi T, Yoshida S, Yamashita M, Matsui S, Kajiyama H, Kotani T. Machine learning prediction models for postpartum depression: A multicenter study in Japan. Journal of Obstetrics and Gynaecology Research 2022;48(7):1775 View
  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;18:174550572211396 View
  25. Qiao D, Liu H, Zhang X, Lei L, Sun N, Yang C, Li G, Guo M, Zhang Y, Zhang K, Liu Z. Exploring the potential of thyroid hormones to predict clinical improvements in depressive patients: A machine learning analysis of the real-world based study.. Journal of Affective Disorders 2022;299:159 View
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  31. Li X, Ono C, Warita N, Shoji T, Nakagawa T, Usukura H, Yu Z, Takahashi Y, Ichiji K, Sugita N, Kobayashi N, Kikuchi S, Kimura R, Hamaie Y, Hino M, Kunii Y, Murakami K, Ishikuro M, Obara T, Nakamura T, Nagami F, Takai T, Ogishima S, Sugawara J, Hoshiai T, Saito M, Tamiya G, Fuse N, Fujii S, Nakayama M, Kuriyama S, Yamamoto M, Yaegashi N, Homma N, Tomita H. Comprehensive evaluation of machine learning algorithms for predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability. Frontiers in Psychiatry 2023;14 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