A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries

TL;DR

This paper presents a recommendation system using deep learning models to personalize content for midwives, aiming to improve their skills and ultimately reduce maternal and neonatal mortality in low-income countries.

Contribution

It introduces a deep learning-based recommendation system tailored for midwives' training content, leveraging behavioral logs to enhance learning outcomes.

Findings

All four evaluated models achieved high prediction accuracy.

The system effectively personalizes content to improve midwives' expertise.

Potential to reduce maternal and neonatal mortality through targeted training.

Abstract

Maternal and child mortality is a public health problem that disproportionately affects low- and middle-income countries. Every day, 800 women and 6,700 newborns die from complications related to pregnancy or childbirth. And for every maternal death, about 20 women suffer serious birth injuries. However, nearly all of these deaths and negative health outcomes are preventable. Midwives are key to revert this situation, and thus it is essential to strengthen their capacities and the quality of their education. This is the aim of the Safe Delivery App, a digital job aid and learning tool to enhance the knowledge, confidence and skills of health practitioners. Here, we use the behavioral logs of the App to implement a recommendation system that presents each midwife with suitable contents to continue gaining expertise. We focus on predicting the click-through rate, the probability that a given user will click on a recommended content. We evaluate four deep learning models and show that all of them produce highly accurate predictions.