# Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach

arXiv: https://arxiv.org/abs/2410.08377 · HTML: https://mlsanity.com/paper/2410.08377/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization](https://mlsanity.com/paper/2310.14526/) (2023)
- [Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making](https://mlsanity.com/paper/2403.15640/) (2024)
- [Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games](https://mlsanity.com/paper/2007.01542/) (2020)
- [Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems](https://mlsanity.com/paper/2103.04730/) (2021)

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

- [Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation](https://mlsanity.com/paper/2501.06103/) (2025)
- [Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents](https://mlsanity.com/paper/2502.10732/) (2025)
- [SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary](https://mlsanity.com/paper/2602.18431/) (2026)
- [Decisions and Deployment: The Five-Year SAHELI Project (2020-2025) on Restless Multi-Armed Bandits for Improving Maternal and Child Health](https://mlsanity.com/paper/2604.07384/) (2026)
- [The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventions](https://mlsanity.com/paper/2609.23374/) (2026)

## Abstract (arXiv)

Maternal mortality remains a significant global public health challenge. One promising approach to reducing maternal deaths occurring during facility-based childbirth is through early warning systems, which require the consistent monitoring of mothers' vital signs after giving birth. Wireless vital sign monitoring devices offer a labor-efficient solution for continuous monitoring, but their scarcity raises the critical question of how to allocate them most effectively. We devise an allocation algorithm for this problem by modeling it as a variant of the popular Restless Multi-Armed Bandit (RMAB) paradigm. In doing so, we identify and address novel, previously unstudied constraints unique to this domain, which render previous approaches for RMABs unsuitable and significantly increase the complexity of the learning and planning problem. To overcome these challenges, we adopt the popular Proximal Policy Optimization (PPO) algorithm from reinforcement learning to learn an allocation policy by training a policy and value function network. We demonstrate in simulations that our approach outperforms the best heuristic baseline by up to a factor of 4.