# Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement

arXiv: https://arxiv.org/abs/2006.07590 · HTML: https://mlsanity.com/paper/2006.07590/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

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

- [Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare](https://mlsanity.com/paper/2105.07965/) (2021)
- [Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes](https://mlsanity.com/paper/2103.09052/) (2021)
- [Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits](https://mlsanity.com/paper/2402.04933/) (2024)
- [Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare](https://mlsanity.com/paper/2305.12640/) (2023)
- [Improving Health Information Access in the World's Largest Maternal Mobile Health Program via Bandit Algorithms](https://mlsanity.com/paper/2407.12131/) (2024)

## Abstract (arXiv)

India accounts for 11% of maternal deaths globally where a woman dies in childbirth every fifteen minutes. Lack of access to preventive care information is a significant problem contributing to high maternal morbidity and mortality numbers, especially in low-income households. We work with ARMMAN, a non-profit based in India, to further the use of call-based information programs by early-on identifying women who might not engage on these programs that are proven to affect health parameters positively.We analyzed anonymized call-records of over 300,000 women registered in an awareness program created by ARMMAN that uses cellphone calls to regularly disseminate health related information. We built robust deep learning based models to predict short term and long term dropout risk from call logs and beneficiaries' demographic information. Our model performs 13% better than competitive baselines for short-term forecasting and 7% better for long term forecasting. We also discuss the applicability of this method in the real world through a pilot validation that uses our method to perform targeted interventions.