# Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs

arXiv: https://arxiv.org/abs/2107.02480 · HTML: https://mlsanity.com/paper/2107.02480/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 71 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://mlsanity.com/paper/1910.03002/) (2019)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [Evaluating time series forecasting models: An empirical study on performance estimation methods](https://mlsanity.com/paper/1905.11744/) (2019)
- [Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles](https://mlsanity.com/paper/1710.02264/) (2017)
- [A Machine-Learning Item Recommendation System for Video Games](https://mlsanity.com/paper/1806.04900/) (2018)
- [Profiling Players with Engagement Predictions](https://mlsanity.com/paper/1907.03870/) (2019)
- [A Time Series Approach To Player Churn and Conversion in Videogames](https://mlsanity.com/paper/2003.10287/) (2020)
- [Forecasting Player Behavioral Data and Simulating in-Game Events](https://mlsanity.com/paper/1710.01931/) (2017)

## Built on by (most cited first, 6 of 6)

- [A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries](https://mlsanity.com/paper/2111.01786/) (2021)
- [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)

Every day, 800 women and 6,700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal and newborn deaths. Data science models together with logs generated by users of online learning applications for midwives can help to improve their learning competencies. The goal is to use these rich behavioral data to push digital learning towards personalized content and to provide an adaptive learning journey. In this work, we evaluate various forecasting methods to determine the interest of future users on the different kind of contents available in the app, broken down by profession and region.