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Application of machine learning in predicting the risk of postpartum depression: A systematic review

  • Published:2022-12 Issue: Volume:318 Page:364-379
  • ISSN:0165-0327
  • Container-title:Journal of Affective Disorders
  • language:en
  • Short-container-title:Journal of Affective Disorders

Author:

Zhong Minhui,Zhang Han,Yu Chan,Jiang Jinxia,Duan Xia

Funder

Tongji University

Science and Technology Commission of Shanghai Municipality

Publisher

Elsevier BV

Subject

Psychiatry and Mental health,Clinical Psychology

Cited by 17 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU;Frontiers in Public Health;2024-05-28

2. Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data;Archives of Women's Mental Health;2024-05-22

3. A Meta-Learning Framework with Model Stacking for Depression Detection Through Sentiment Classification;2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS);2024-04-18

4. Predictive Algorithms for Early Postpartum Depression Detection: CatBoost vs. LightGBM;2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO);2024-03-14

5. The Future of Prediction Modeling in Clinical Practice for Obstetrics and Gynecology;Obstetrics & Gynecology;2024-03
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