Abstract
Background
Perinatal mental health significantly affects mothers, infants, and families. Despite their resilience and strengths, Aboriginal mothers experience disproportionate physical and mental health disparities. These result from historical and ongoing impacts of colonization and the resultant complex trauma. Conventional approaches to perinatal care present many barriers for Aboriginal mothers who frequently feel disengaged, apprehensive and unsafe. Current score-based risk-screening practices that algorithmically drive referrals, further ingrain fears including culturally biased judgments and child removal. The Baby Coming You Ready (BCYR) model of care centred around a digitised, holistic, strengths-based assessment, was co-designed to address these barriers. The recent successful pilot demonstrated BCYR effectively replaced all current risk-based screens. However, many professionals disproportionately rely on psychological risk scores, overlooking the contextual circumstances of Aboriginal mothers, their cultural strengths and mitigating protective factors.
Methods
To address this singular reliance screening psychometrics whilst supporting strengthened culturally considered clinical assessment, we propose a culturally sensitive eXplainable AI (XAI) solution. It combines XAI with Aboriginal lived experience, knowledge and wisdom to generate a clinical prediction model to support professionals and Aboriginal mothers being screened. The XAI solution can identify, prioritise, and weigh both maternal protective strengths and risk factors, quantify their relative impacts on perinatal mental-health and well-being at both group and individual levels.
Results
Different machine learning algorithms, including Random Forest, K-nearest neighbour, and support vector machine, alongside glassbox Explainable Boosting Machine (EBM) models, were trained on the real life de-identified data generated during the BCYR pilot. Additionally, XAI techniques like SHAP and LIME are utilised for interpretability on black box models. Results show the EBM model demonstrates superior performance in prediction, with an accuracy of 0.849, F1 score of 0.771 and AUC of 0.821. Global explanations across the entire dataset and local explanations for individual cases, achieved through different methods, were compared and showed similar and stable results.
Conclusions
This study demonstrated the potential for this XAI solution to enhance professionals' capability in culturally responsive clinical reasoning in perinatal mental-health screening to improve experience and strengthen outcomes for Aboriginal women.