Development and Validation of a Context-Sensitive Predictive Model for Maternal Risk Using Multidimensional Data

Maternal mortality remains a serious public health concern, particularly in low- and middle-income countries, where many maternal deaths are still preventable. In many settings, risk is identified too late, often after complications have already become severe. This project was developed in response to that gap. The purpose of this study is to design and validate a context-sensitive predictive model that can support earlier identification of maternal risk during pregnancy and the postpartum period. Rather than relying only on clinical indicators, the project brings together clinical, social, and health system factors that are known to shape maternal outcomes. This approach reflects the reality of maternal care, where risk is influenced not only by biology, but also by access to care, service quality, and social conditions. The study builds directly on a preceding scoping review that mapped existing maternal risk prediction models and highlighted key limitations. Most existing models focus narrowly on medical variables, show limited external validation, and pay little attention to equity, interpretability, or real-world use. This project aims to address those gaps by grounding model development in a multidimensional conceptual framework and by using routinely collected maternal health data. The expected outcome of the project is a validated predictive model that can estimate maternal risk in a transparent and interpretable way. The model is intended to support clinical decision-making and maternal health planning rather than replace professional judgment. In addition, the study will generate methodological guidance on ethical, equitable, and context-aware use of machine learning in maternal health. Overall, this project seeks to contribute both a practical tool and a clearer pathway for translating predictive modeling into meaningful improvements in maternal care.