AI for Public Health

The maternal mortality rate in India is appalling, largely fueled by lack of access to preventive care information, especially in low resource households. We partner with non-profit, ARMMAN, that aims to use mobile health technologies to improve the maternal and child health outcomes.

 

To assisst ARMMAN and such non-profits, we develop a Restless Multi-Armed Bandit (RMAB) based solution to help improve accessibility of critical health information, via increased engagement of beneficiaries with their program. We address fundamental research challenges that crop up along the way and present technical advances in RMABs and Planning Algorithms for Limited-Resource Allocation. Transcending the boundaries of typical laboratory research, we also deploy our models in the field, and present results from a first-of-its-kind pilot test employing and evaluating RMABs in a real-world public health application.

We introduce robustness in restless multi-armed bandits (RMABs), a popular model for constrained resource allocation among independent stochastic processes (arms). Nearly all RMAB techniques assume stochastic dynamics are precisely known. However, in many real-world settings, dynamics are estimated with significant uncertainty, e.g., via historical data, which can lead to bad outcomes if ignored. To address this, we develop an algorithm to compute minimax regret--robust policies for RMABs. Our approach uses a double oracle framework (oracles for agent and nature), which is often used for single-process robust planning but requires significant new techniques to accommodate the combinatorial nature of RMABs. Specifically, we design a deep reinforcement learning (RL) algorithm, DDLPO, which tackles the combinatorial challenge by learning an auxiliary "λ-network" in tandem with policy networks per arm, greatly reducing sample complexity, with guarantees on convergence. DDLPO, of general interest, implements our reward-maximizing agent oracle. We then tackle the challenging regret-maximizing nature oracle, a non-stationary RL challenge, by formulating it as a multi-agent RL problem between a policy optimizer and adversarial nature. This formulation is of general interest---we solve it for RMABs by creating a multi-agent extension of DDLPO with a shared critic. We show our approaches work well in three experimental domains.

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake. One of the United Nations’ sustainable development goals (SDG 3) aims to end preventable deaths of new-borns and children under five years of age. We focus on Nigeria, where the rate of infant mortal-ity is appalling. We collaborate with HelpMum, a large non-profit organization in Nigeria to design and optimize the allocation of heterogeneous health interventions under uncertainty to increase vaccination uptake, the first such collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination Intervention Optimiser, is based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination. Our optimization formulation is intractable in practice. We present a heuristic approach that enables us to solve the problem for real-world use-cases. We also present theoretical bounds for the heuristic method. Finally, we show that the proposed approach out-performs baseline methods in terms of vaccination uptake through experimental evaluation. HelpMum is currently planning a pilot program based on our approach to be deployed in the largest city of Nigeria, which would be the first deployment of an AI-driven vaccination uptake program in the country and hopefully, pave the way for other data-driven programs to improve health outcomes in Nigeria.

In the past decade, breakthroughs of Artificial Intelligence (AI) in its multiple sub-area have made new applications in various domains possible. One typical yet essential example is the public health domain. There are many challenges for humans in our never-ending battle with diseases. Among them, problems involving harnessing data with network structures and future planning, such as disease control or resource allocation, demand effective solutions significantly. However, unfortunately, some of them are too complicated or unscalable for humans to solve optimally. This thesis tackles these challenging sequential network planning problems for the public health domain by advancing the state-of-the-art to a new level of effectiveness.

In particular, My thesis provides three main contributions to overcome the emerging challenges when applying sequential network planning problems in the public health domain, namely (1) a novel sequential network-based screening/contact tracing framework under uncertainty, (2) a novel sequential network-based mobile interventions framework, (3) theoretical analysis, algorithmic solutions and empirical experiments that shows superior performance compared to previous approaches both theoretically and empirically.

More concretely, the first part of this thesis studies the active screening problem as an emerging application for disease prevention. I introduce a new approach to modeling multi-round network-based screening/contact tracing under uncertainty. Based on the well-known network SIS model in computational epidemiology, which is applicable for many diseases, I propose a model of the multi-agent active screening problem (ACTS) and prove its NP-hardness. I further proposed the REMEDY (REcurrent screening Multi-round Efficient DYnamic agent) algorithm for solving this problem. With a time and solution quality trade-off, REMEDY has two variants, Full- and Fast-REMEDY. It is a Frank-Wolfe-style gradient descent algorithm realized by compacting the representation of belief states to represent uncertainty. As shown in the experiment conducted, Full- and Fast-REMEDY are not only being superior in controlling diseases to all the previous approaches; they are also robust to varying levels of missing
information in the social graph and budget change, thus enabling
the use of our agent to improve the current practice of real-world
screening contexts.

The second part of this thesis focuses on the scalability issue for the time horizon for the ACTS problem. Although Full-REMEDY provides excellent solution qualities, it fails to scale to large time horizons while fully considering the future effect of current interventions. Thus, I proposed a novel reinforcement learning (RL) approach based on Deep Q-Networks (DQN). Due to the nature of the ACTS problem, several challenges that the traditional RL can not handle have emerged, including (1) the combinatorial nature of the problem, (2) the need for sequential planning, and (3) the uncertainties in the infectiousness states of the population. I design several innovative adaptations in my RL approach to address the above challenges. I will introduce why and how these adaptations are made in this part.

For the third part, I introduce a novel sequential network-based mobile interventions framework. It is a restless multi-armed bandits (RMABs) with network pulling effects. In the proposed model, arms are partially recharging and connected through a graph. Pulling one arm also improves the state of neighboring arms, significantly extending the previously studied setting of fully recharging bandits with no network effects. Such network effect may arise due to regular population movements (such as commuting between home and work) for mobile intervention applications. In my thesis, I show that network effects in RMABs induce strong reward coupling that is not accounted for by existing solution methods. I also propose a new solution approach for the networked RMABs by exploiting concavity properties that arise under natural assumptions on the structure of intervention effects. In addition, I show the optimality of such a method in idealized settings and demonstrate that it empirically outperforms state-of-the-art baselines.

The widespread availability of cell phones has enabled nonprofits to deliver critical health information to their beneficiaries in a timely manner. This paper describes our work to assist non-profits that employ automated messaging programs to deliver timely preventive care information to beneficiaries (new and expecting mothers) during pregnancy and after delivery. Unfortunately, a key challenge in such information delivery programs is that a significant fraction of beneficiaries drop out of the program. Yet, non-profits often have limited health-worker resources (time) to place crucial service calls for live interaction with beneficiaries to prevent such engagement drops. To assist non-profits in optimizing this limited resource, we developed a Restless Multi-Armed Bandits (RMABs) system. One key technical contribution in this system is a novel clustering method of offline historical data to infer unknown RMAB parameters. Our second major contribution is evaluation of our RMAB system in collaboration with an NGO, via a real-world service quality improvement study. The study compared strategies for optimizing service calls to 23003 participants over a period of 7 weeks to reduce engagement drops. We show that the RMAB group provides statistically significant improvement over other comparison groups, reducing ∼ 30% engagement drops. To the best of our knowledge, this is the first study demonstrating the utility of RMABs in real world public health settings. We are transitioning our RMAB system to the NGO for real-world use.