# Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings

arXiv: https://arxiv.org/abs/2408.07629 · HTML: https://mlsanity.com/paper/2408.07629/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Revisiting Fundamentals of Experience Replay](https://mlsanity.com/paper/2007.06700/) (2020)
- [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://mlsanity.com/paper/1802.09127/) (2018)
- [Neural Contextual Bandits with Deep Representation and Shallow Exploration](https://mlsanity.com/paper/2012.01780/) (2020)
- [Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare](https://mlsanity.com/paper/2105.07965/) (2021)
- [Power Constrained Bandits](https://mlsanity.com/paper/2004.06230/) (2020)
- [Unbiased split variable selection for random survival forests using maximally selected rank statistics](https://mlsanity.com/paper/1605.03391/) (2016)
- [A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit](https://mlsanity.com/paper/1510.00757/) (2015)
- [Efficient Online Bayesian Inference for Neural Bandits](https://mlsanity.com/paper/2112.00195/) (2021)
- [Online Limited Memory Neural-Linear Bandits with Likelihood Matching](https://mlsanity.com/paper/2102.03799/) (2021)
- [Policy Optimization for Personalized Interventions in Behavioral Health](https://mlsanity.com/paper/2303.12206/) (2023)
- [Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes](https://mlsanity.com/paper/2103.09052/) (2021)
- [Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health](https://mlsanity.com/paper/2308.09726/) (2023)
- [Survival trees for left-truncated and right-censored data, with application to time-varying covariate data](https://mlsanity.com/paper/1606.03033/) (2016)

## Abstract (arXiv)

By providing evidence-based clinical decision support, digital tools and electronic health records can revolutionize patient management, especially in resource-poor settings where fewer health workers are available and often need more training. When these tools are integrated with AI, they can offer personalized support and adaptive interventions, effectively connecting community health workers (CHWs) and healthcare facilities. The CHARM (Community Health Access & Resource Management) app is an AI-native mobile app for CHWs. Developed through a joint partnership of Causal Foundry (CF) and mothers2mothers (m2m), CHARM empowers CHWs, mainly local women, by streamlining case management, enhancing learning, and improving communication. This paper details CHARM's development, integration, and upcoming reinforcement learning-based adaptive interventions, all aimed at enhancing health worker engagement, efficiency, and patient outcomes, thereby enhancing CHWs' capabilities and community health.