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Applying Supervised Machine Learning to Develop an Adaptive Risk-Scoring Tool to Predict Maternal Morbidity and Adverse Pregnancy Outcomes

Geoffrey Arunga of BroadReach in Kenya will develop a digital assessment tool to identify women with the highest risk of maternal morbidity and adverse pregnancy outcomes, and their causes, and to inform clinicians and health policies to improve maternal health and survival. They will apply advanced statistical analyses and machine learning techniques to clinical, social, and economic data from an existing longitudinal study of pregnant women in West Africa to identify the data that can best predict risk. This will be used to derive a minimum set of questions that can be incorporated into a digital tool for health workers to assess a woman’s risk at any given timepoint during pregnancy. The tool will be pilot tested for feasibility and predictive performance in rural and urban-based health facilities in Ghana.

More information about Data Science Approaches to Improve Maternal and Child Health in Africa

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