The world’s oldest living culture leads the way with interpretable generative AI: Aboriginal women shaping the future of perinatal mental health services

Despite their strengths, Aboriginal mothers experience significant health disparities, including perinatal mental health challenges influenced by social, historical, and cultural determinants. Routine risk screening informs follow-up care, yet conventional approaches often fail to address cultural considerations. The Baby Coming You Ready (BCYR) program provides a strengths-based, culturally adapted digital assessment designed to enhance perinatal mental health care. However, some clinicians continue to rely primarily on conventional risk scores, which can neglect broader contextual circumstances influencing Aboriginal mothers, including cultural strengths and protective factors, thereby limiting the program's full impact. This exploratory model-development study examined whether generative AI data augmentation combined with an intrinsically interpretable machine-learning model could support culturally contextualised prediction of perinatal psychological distress among Aboriginal mothers. We analysed deidentified BCYR pilot data from 293 Aboriginal mothers across six Western Australian sites. After excluding five records with incomplete outcome data, the analytic dataset included 288 participants. Models were evaluated using repeated internal train-test splits, with synthetic data used only for training augmentation and performance assessed on held-out real test data. The highest internal performance was observed for the TVAE-augmented Explainable Boosting Machine at an augmentation ratio of 1.0, with an accuracy of 0.867, an F1 score of 0.817, and an AUC of 0.934. Model explanations identified self-blame, loneliness, family pride, day-to-day management, sleep, and related contextual factors as influential contributors to model-estimated risk. These explanations are intended as prompts for culturally safe discussion and clinical reflection. Findings suggest that interpretable generative AI may have potential to support culturally responsive perinatal mental health assessment. However, broader community-governed external validation and prospective clinical workflow evaluation are required before clinical implementation.

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Publication Details

Journal
PLOS Digital Health
Published
2026-10-15
DOI
https://doi.org/10.1371/journal.pdig.0001709
Primary Topic
Maternal Mental Health During Pregnancy and Postpartum
Type
article
Field-Weighted Citation Impact
0.00

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article

The world’s oldest living culture leads the way with interpretable generative AI: Aboriginal women shaping the future of perinatal mental health services

Ferdous Ahmed Sohel, Rhonda Marriott, Jayne Kotz, Guanjin Wang et al.
PLOS Digital Health
Maternal Mental Health During Pregnancy and Postpartum
article

The world’s oldest living culture leads the way with interpretable generative AI: Aboriginal women shaping the future of perinatal mental health services

Ferdous Ahmed Sohel, Rhonda Marriott, Jayne Kotz, Guanjin Wang, Corinne L. Reid, Bridgette Kelly, Rehan Raza, Tracy Reibel, Trish Ratajczak, Hachem Bennamoun, Wai Hang Kwok, Roz Walker
article en

Abstract

Despite their strengths, Aboriginal mothers experience significant health disparities, including perinatal mental health challenges influenced by social, historical, and cultural determinants. Routine risk screening informs follow-up care, yet conventional approaches often fail to address cultural considerations. The Baby Coming You Ready (BCYR) program provides a strengths-based, culturally adapted digital assessment designed to enhance perinatal mental health care. However, some clinicians continue to rely primarily on conventional risk scores, which can neglect broader contextual circumstances influencing Aboriginal mothers, including cultural strengths and protective factors, thereby limiting the program's full impact. This exploratory model-development study examined whether generative AI data augmentation combined with an intrinsically interpretable machine-learning model could support culturally contextualised prediction of perinatal psychological distress among Aboriginal mothers. We analysed deidentified BCYR pilot data from 293 Aboriginal mothers across six Western Australian sites. After excluding five records with incomplete outcome data, the analytic dataset included 288 participants. Models were evaluated using repeated internal train-test splits, with synthetic data used only for training augmentation and performance assessed on held-out real test data. The highest internal performance was observed for the TVAE-augmented Explainable Boosting Machine at an augmentation ratio of 1.0, with an accuracy of 0.867, an F1 score of 0.817, and an AUC of 0.934. Model explanations identified self-blame, loneliness, family pride, day-to-day management, sleep, and related contextual factors as influential contributors to model-estimated risk. These explanations are intended as prompts for culturally safe discussion and clinical reflection. Findings suggest that interpretable generative AI may have potential to support culturally responsive perinatal mental health assessment. However, broader community-governed external validation and prospective clinical workflow evaluation are required before clinical implementation.

PLOS Digital HealthVol. 5(10)
Edith Cowan University (AU), Murdoch University (AU)
Google, Western Australian Future Health and Innovation Fund, Government of Western Australia
Good health and well-being, Reduced inequalities
Openalex Percentile: Top 11%
Maternal Mental Health During Pregnancy and Postpartum
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