A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities
This addresses healthcare disparities for vulnerable populations in resource-limited settings, but it appears incremental as it focuses on platform architecture and data management rather than novel algorithmic breakthroughs.
The paper tackles the problem of reducing health inequalities in low- and middle-income countries by developing a data-centric machine learning platform that uses behavioral logs from mobile health apps to provide recommendations and predictions for front-line health workers, aiming to improve health outcomes such as preventing maternal and newborn deaths.
Providing front-line health workers in low- and middle- income countries with recommendations and predictions to improve health outcomes can have a tremendous impact on reducing healthcare inequalities, for instance by helping to prevent the thousands of maternal and newborn deaths that occur every day. To that end, we are developing a data-centric machine learning platform that leverages the behavioral logs from a wide range of mobile health applications running in those countries. Here we describe the platform architecture, focusing on the details that help us to maximize the quality and organization of the data throughout the whole process, from the data ingestion with a data-science purposed software development kit to the data pipelines, feature engineering and model management.