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Providing front-line health workers in low- and middle- income countries withrecommendations and predictions to improve health outcomes can have atremendous impact on reducing healthcare inequalities, for instance by helpingto prevent the thousands of maternal and newborn deaths that occur every day.To that end, we are developing a data-centric machine learning platform thatleverages the behavioral logs from a wide range of mobile health applicationsrunning in those countries. Here we describe the platform architecture,focusing on the details that help us to maximize the quality and organizationof the data throughout the whole process, from the data ingestion with adata-science purposed software development kit to the data pipelines, featureengineering and model management.

A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities