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

arXiv: https://arxiv.org/abs/2111.01786 · HTML: https://mlsanity.com/paper/2111.01786/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs](https://mlsanity.com/paper/2107.02480/) (2021)
- [A Machine-Learning Item Recommendation System for Video Games](https://mlsanity.com/paper/1806.04900/) (2018)

## Built on by (most cited first, 5 of 5)

- [User Engagement in Mobile Health Applications](https://mlsanity.com/paper/2206.08178/) (2022)
- [Actionable Recourse via GANs for Mobile Health](https://mlsanity.com/paper/2211.06525/) (2022)
- [The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems](https://mlsanity.com/paper/2409.16098/) (2024)
- [Synthetic Data Generator for Adaptive Interventions in Global Health](https://mlsanity.com/paper/2303.01954/) (2023)
- [Adaptive Interventions for Global Health: A Case Study of Malaria](https://mlsanity.com/paper/2303.02075/) (2023)

## Abstract (arXiv)

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.