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

TL;DR

This paper presents a data-centric machine learning platform designed to improve health outcomes and reduce inequalities in low- and middle-income countries by leveraging behavioral logs from mobile health applications.

Contribution

The paper introduces a novel platform architecture that enhances data quality and organization for machine learning in healthcare settings in developing countries.

Findings

Improved data ingestion and management processes.

Enhanced model accuracy through better feature engineering.

Potential to significantly reduce health inequalities.

Abstract

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.