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

Machine Learning (2027) Clinical Informatics (1288) Perinatal Depression; Postpartum Depression; PPD (131) Development and Evaluation of Research Methods, Instruments and Tools (718) Methods and New Tools in Mental Health Research (543) Diagnostic Tools in Mental Health (318) Applications of AI (268) Clinical Mental Health Informatics (60)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58649, first published .
Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study

Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study

Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study

Authors of this article:

Ren Zhang1, 2 Author Orcid Image ;   Yi Liu3 Author Orcid Image ;   Zhiwei Zhang2 Author Orcid Image ;   Rui Luo4 Author Orcid Image ;   Bin Lv1 Author Orcid Image
Article Authors Cited by (7) Tweetations Metrics

Journals

  1. Xie K, Jiang S, Wang Y, Chen H, Wu X, Xu B. Association of immune-inflammatory biomarkers during pregnancy and the postpartum period with postpartum depression symptoms: A cross-sectional and longitudinal retrospective analysis. Brain, Behavior, and Immunity 2025;129:42 View
  2. Li Y, Xiao M, Li Y, Lv L, Zhang S, Liu Y, Zhang J. Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation. JMIR Medical Informatics 2025;13:e72349 View
  3. García-Méndez S, de Arriba-Pérez F. Detecting and Explaining Postpartum Depression in Real-Time with Generative Artificial Intelligence. Applied Artificial Intelligence 2025;39(1) View
  4. Xia J, Chen C, Lu X, Zhang T, Wang T, Wang Q, Zhou Q. Artificial intelligence-oriented predictive model for the risk of postpartum depression: a systematic review. Frontiers in Public Health 2025;13 View
  5. Ba H, Du H, Cheng C, Zhang Y, Ruan L, Chen Z. Identification of Syndrome Types in Patients With Pancreatic Cancer From Free Text in Electronic Medical Records: Model Development and Validation. JMIR Formative Research 2025;9:e70602 View
  6. Olivé-Mas J, Aguilar E, Tost M, Martí L, Giménez C, Lesmes C, Moreno-Baró A, Aparicio M, Agut M, Olasz I, Cobo J. Early-Onset and Delayed-Onset Postpartum Psychosis: A Case Series. Women 2025;5(4):36 View

Conference Proceedings

  1. Mathew J, Ramasamy G. 2025 International Conference on Emerging Technologies in Computing and Communication (ETCC). Graph Convolutional Networks for Predicting Postpartum Depression: A Symptom-Based Analysis View