Interpretable Predictive Modelling for Multi-domain Healthcare Outcomes and Insights
Abstract: Modern healthcare faces a critical need for predictive models that can reliably guide clinical decisions, yet many state-of-the-art artificial intelligence (AI) approaches remain “black boxes”. Current machine learning (ML) and deep learning (DL) models often achieve high accuracy but provide limited transparency. They typically predict outcomes without explaining why they occur or how those outcomes would change under different interventions (the “what-if” scenarios). Furthermore, models trained on narrow datasets often fail to generalize across different hospitals or patient populations, limiting their real-world reliability. These challenges call for a shift from focusing solely on accuracy to developing decision-oriented AI that is transparent and interpretable.