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

Artificial Intelligence (2129) Vulnerable Populations in Health Research (237) Equity and Digital Divide (370) Machine Learning (2076) Perinatal Depression; Postpartum Depression; PPD (133)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68030, first published .
Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach

Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach

Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach

Authors of this article:

Guanjin Wang1, 2 Author Orcid Image ;   Hachem Bennamoun1 Author Orcid Image ;   Wai Hang Kwok3 Author Orcid Image ;   Jenny Paola Ortega Quimbayo1 Author Orcid Image ;   Bridgette Kelly2 Author Orcid Image ;   Trish Ratajczak2 Author Orcid Image ;   Rhonda Marriott2 Author Orcid Image ;   Roz Walker2 Author Orcid Image ;   Jayne Kotz2 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Zhang M, Zhong M, Cheng Y, Zhang T. Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study. JMIR Medical Informatics 2025;13:e74940 View
  2. Sheng A, Yu T, Yu Y, Zhang M, Zeng Y, Ye L. Analysis of the integration of medical and nursing care and its association with depression and anxiety among medical staff in the Jinhua area. Frontiers in Psychiatry 2025;16 View