Use of artificial intelligence based models for learning better policy for maternal and child health

Abstract

More than 640,000 babies died of sepsis before they reach the age of one month in India in 2016. Despite a large number of government schemes aimed at reducing this rate, this number still remains high because of the complexity and interplay of factors involved. Finding an optimum policy and solutions to this problem needs learning from data. We integrated diverse sources of data and applied Bayesian Artificial Intelligence methods for learning to mitigate sepsis and adverse pregnancy outcomes in India. In this project, we created models that combine the robustness of ensemble averaged Baeysian Networks with decision learning and impact evaluation by using simulations and counterfactual reasoning respectively. We will demonstrate the process of learning these models and how these led us to infer the pivotal role of Water, Sanitation and Hygiene for reducing Adverse Pregnancy Outcome and neonatal sepsis in the population studied. We will also demonstrate the creation of explainable AI models for complex public health challenges and their deployment with wiseR, our in-house, open source platform for doing end-to-end Bayesian Decision Network learning.