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AI for Public Health

Modeling to Inform Disease Control, Screening, Treatment Policies, and Prevention Interventions

 

  

MOTIVATION

AI tools can be used to inform public health policy.  For example, predictive analytics can be used to identify risk factors for disease; and optimization frameworks (whether single stage or repeated) can be used to identify when to screen or treat disease, or which risk groups to target given limited resources. Optimization frameworks can also be used to channel limited resources towards at-risk individuals to improve their adherence to healthy habits. We describe several projects and potential project areas below.

  

CURRENT PROJECTS

HIV Prevention
Among Homeless Youth

Homeless child


This project focuses on the development of decision support systems for homeless youth drop-in center staff, who need to find the most influential homeless youth to raise awareness about HIV (and other STDs) among their peers, and to drive the homeless youth community towards safer behaviors.  View HIV Prevention among Homeless Youth by Influence Maximization.

  

Improving Maternal and
Child Health Outcomes in
Partnership with ARMMAN

Maternal and Child Health

This project aims at using AI for improving Maternal and Child Health Outcomes by predicting which beneficiaries are at a risk of dropping out from automated health information delivery programs. View this page for more info. 

 

 

Combatting COVID-19

Coronavirus

The COVID-19 outbreak has caused an unprecedented global reaction with countries taking drastic steps to combat the pandemic. Mathematical modeling and multi-agent based analysis of the pandemic allows better understanding of the disease spread and may help inform policy at the national and regional level. We use tools and modeling techniques from AI to help understand the situation better and design aids that may help policymakers design better solutions in the fight against this pandemic. Click here to read more about Teamcore's efforts to stop the spread of COVID-19.

  

Using Machine Learning
& Multi-Agent Planning
to Fight Tuberculosis

Tuberculosis Health Post

Fighting and Preventing Tuberculosis in India

Tuberculosis is one of the top 10 killers in the world and is especially prevalent in India. AI can help across the entire pipeline of care, from decision support tools for planning active screening routes, to predictive algorithms for resource constrained health workers to deliver targeted interventions to patients.

  

Using Social Networks
for Prevention Interventions

Click here to read more on this initiative.

Two young men

Substance abuse prevention among homeless youth

Research has consistently documented levels of cocaine, heroin, methamphetamine, alcohol, and marijuana use and abuse among these adolescents that far exceed that of housed adolescents.  This Social Networks and Substance Abuse Prevention for Homeless Youth project aims to use algorithms to determine the best group formations to prevent regular use of hard drugs among homeless youth.

 

Army officer with a young child

Suicide prevention among active duty military and homeless youth

One of the fundamental questions facing social science is how social networks and the cognitions people have about their networks affect their mental states and mental health.  AI techniques present an opportunity to dynamically model social networks and the messages transmitted across those networks to create predictive models of influence unavailable with standard statistical techniques.  View Predictive Modeling for Early Identification of Suicidal Thinking.

  

  

PROJECT
PARTICIPANTS

Sze-chuan Suen
Milind Tambe
Bryan Wilder
Han Ching Ou
Dana Goldman

Eric Rice
Carl Castro
Anthony Fulginiti
Anamika Barman-Adhikari
Aditya Mate

Phebe Vayanos
Aida Rahmattalabi
Jackson Killian

  

SPONSORS

California HIV/AIDS Research Program
The California HIV/AIDS Research Program (CHRP) logo

Army Research Office

  

RELATED
PUBLICATIONS

Aditya Mate*, Lovish Madaan*, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham, and Milind Tambe. 2/2022. “Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health.” In AAAI Conference on Artificial Intelligence. Vancouver, Canada.Abstract
aaai_rmab_armman_camready.pdf
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.
Aditya Mate*, Lovish Madaan*, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham, and Milind Tambe. 12/2021. “Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes.” In MLPH: Machine Learning in Public Health NeurIPS 2021 Workshop.Abstract
neurips-workshop-mlph-restlessbandits.pdf

The widespread availability of cell phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. This paper describes our work in assisting non-profits employing automated messaging programs to deliver timely preventive care information to 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 tend to drop out. 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 system to the NGO for real-world use.

Aditya Mate, Andrew Perrault, and Milind Tambe. 5/7/2021. “Risk-Aware Interventions in Public Health: Planning with Restless Multi-Armed Bandits.” In 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). London, UK.Abstract
Risk-Aware-Bandits.pdf
Community Health Workers (CHWs) form an important component of health-care systems globally, especially in low-resource settings. CHWs are often tasked with monitoring the health of and intervening on their patient cohort. Previous work has developed several classes of Restless Multi-Armed Bandits (RMABs) that are computationally tractable and indexable, a condition that guarantees asymptotic optimality, for solving such health monitoring and intervention problems (HMIPs).
However, existing solutions to HMIPs fail to account for risk-sensitivity considerations of CHWs in the planning stage and may run the danger of ignoring some patients completely because they are deemed less valuable to intervene on.
Additionally, these also rely on patients reporting their state of adherence accurately when intervened upon. Towards tackling these issues, our contributions in this paper are as follows: 
(1) We develop an RMAB solution to HMIPs that allows for reward functions that are monotone increasing, rather than linear, in the belief state and also supports a wider class of observations.
(2) We prove theoretical guarantees on the asymptotic optimality of our algorithm for any arbitrary reward function. Additionally, we show that for the specific reward function considered in previous work, our theoretical conditions are stronger than the state-of-the-art guarantees.
(3) We show the applicability of these new results for addressing the three issues pertaining to: risk-sensitive planning, equitable allocation and reliance on perfect observations as highlighted above. We evaluate these techniques on both simulated as well as real data from a prevalent CHW task of monitoring adherence of tuberculosis patients to their prescribed medication in Mumbai, India and show improved performance over the state-of-the-art. The simulation code is available at: https://github.com/AdityaMate/risk-aware-bandits.
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