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

Journals

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  9. Saqib K, Khan A, Butt Z. Machine Learning Methods for Predicting Postpartum Depression: Scoping Review. JMIR Mental Health 2021;8(11):e29838 View
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  11. Liu H, Dai A, Zhou Z, Xu X, Gao K, Li Q, Xu S, Feng Y, Chen C, Ge C, Lu Y, Zou J, Wang S. An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches. Journal of Affective Disorders 2023;328:163 View
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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. 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 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
  26. Cho K, Choi J, Han S. Validation of depression determinants in caregivers of dementia patients with machine learning algorithms and statistical model. Frontiers in Medicine 2023;10 View
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  28. Belciug S. Artificial Intelligence and the second trimester of pregnancy: A literature survey. Intelligent Decision Technologies 2023;17(1):263 View
  29. Qi W, Wang Y, Li C, He K, Wang Y, Huang S, Li C, Guo Q, Hu J. Predictive models for predicting the risk of maternal postpartum depression: A systematic review and evaluation. Journal of Affective Disorders 2023;333:107 View
  30. Wakefield C, Frasch M. Predicting Patients Requiring Treatment for Depression in the Postpartum Period Using Common Electronic Medical Record Data Available Antepartum. AJPM Focus 2023;2(3):100100 View
  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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  33. Lilhore U, Dalal S, Faujdar N, Simaiya S, Dahiya M, Tomar S, Hashmi A. Unveiling the prevalence and risk factors of early stage postpartum depression: a hybrid deep learning approach. Multimedia Tools and Applications 2024;83(26):68281 View
  34. Choi J, Kim K, Park S, Hur J, Yang H, Kim Y, Lee H, Han S. Investigation of factors regarding the effects of COVID-19 pandemic on college students’ depression by quantum annealer. Scientific Reports 2024;14(1) View
  35. Heyat M, Akhtar F, Munir F, Sultana A, Muaad A, Gul I, Sawan M, Asghar W, Iqbal S, Baig A, de la Torre Díez I, Wu K. Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function. Complex & Intelligent Systems 2024;10(4):5883 View
  36. Razavi M, Ziyadidegan S, Mahmoudzadeh A, Kazeminasab S, Baharlouei E, Janfaza V, Jahromi R, Sasangohar F. Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review. JMIR Mental Health 2024;11:e53714 View
  37. Sadjadpour F, Hosseinichimeh N, Abedi V, Soghier L. Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU. Frontiers in Public Health 2024;12 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