AI for Social Work, Public Health, and Medical Decision Making

Social network

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

  

MOTIVATION

AI tools can be used to inform social work, public health policy, and medical decision making.  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.  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.

  

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, 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
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.
Aditya Mate*, Jackson A. Killian*, Haifeng Xu, Andrew Perrault, and Milind Tambe. 12/5/2020. “Collapsing Bandits and Their Application to Public Health Interventions.” In Advances in Neural and Information Processing Systems (NeurIPS) 12/5/2020. Vancouver, Canada. Publisher's VersionAbstract
We propose and study Collapsing Bandits, a new restless multi-armed bandit (RMAB) setting in which each arm follows a binary-state Markovian process with a special structure: when an arm is played, the state is fully observed, thus “collapsing” any uncertainty, but when an arm is passive, no observation is made, thus allowing uncertainty to evolve. The goal is to keep as many arms in the “good” state as possible by planning a limited budget of actions per round. Such Collapsing Bandits are natural models for many healthcare domains in which health workers must simultaneously monitor patients and deliver interventions in a way that maximizes the health of their patient cohort. Our main contributions are as follows: (i) Building on the Whittle index technique for RMABs, we derive conditions under which the Collapsing Bandits problem is indexable. Our derivation hinges on novel conditions that characterize when the optimal policies may take the form of either “forward” or “reverse” threshold policies. (ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed form. (iii) We evaluate our algorithm on several data distributions including data from a real-world healthcare task in which a worker must monitor and deliver interventions to maximize their patients’ adherence to tuberculosis medication. Our algorithm achieves a 3-order-of-magnitude speedup compared to state-of-the-art RMAB techniques, while achieving similar performance.
Evaluating COVID-19 Lockdown and Business-Sector-Specific Reopening Policies for Three US States
Jackson A. Killian, Marie Charpignon, Bryan Wilder, Andrew Perrault, Milind Tambe, and Maimuna S. Majumder. 8/24/2020. “Evaluating COVID-19 Lockdown and Business-Sector-Specific Reopening Policies for Three US States.” In KDD 2020 Workshop on Humanitarian Mapping. Publisher's VersionAbstract
Background: The United States has been particularly hard-hit by COVID-19, accounting for approximately 30% of all global cases and deaths from the disease that have been reported as of May 20, 2020. We extended our agent-based model for COVID-19 transmission to study the effect of alternative lockdown and reopening policies on disease dynamics in Georgia, Florida, and Mississippi. Specifically, for each state we simulated the spread of the disease had the state enforced its lockdown approximately one week earlier than it did. We also simulated Georgia's reopening plan under various levels of physical distancing if enacted in each state, making projections until June 15, 2020.

Methods: We used an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model was first calibrated to each state using publicly available COVID-19 death data as of April 23, then implemented to simulate given lockdown or reopening policies.

Results: Our model estimated that imposing lockdowns one week earlier could have resulted in hundreds fewer COVID-19-related deaths in the context of all three states. These estimates quantify the effect of early action, a key metric to weigh in developing prospective policies to combat a potential second wave of infection in each of these states. Further, when simulating Georgia’s plan to reopen select businesses as of April 27, our model found that a reopening policy that includes physical distancing to ensure no more than 25% of pre-lockdown contact rates at reopened businesses could allow limited economic activity to resume in any of the three states, while also eventually flattening the curve of COVID-19-related deaths by June 15, 2020.
Amulya Yadav, Leandro Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, and Heather Carmichael. 2015. “Preventing HIV Spread in Homeless Populations Using PSINET.” In Conference on Innovative Applications of Artificial Intelligence (IAAI-15).Abstract
Homeless youth are prone to HIV due to their engagement in high risk behavior. Many agencies conduct interventions to educate/train a select group of homeless youth about HIV prevention practices and rely on word-of-mouth spread of information through their social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network’s structure and in the evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision support system to aid the agencies in this task. PSINET includes the following key novelties: (i) it handles uncertainties in network structure and evolving network state; (ii) it addresses these uncertainties by using POMDPs in influence maximization; (iii) it provides algorithmic advances to allow high quality approximate solutions for such POMDPs. Simulations show that PSINET achieves ∼60% more information spread over the current state-of-the-art. PSINET was developed in collaboration with My Friend’s Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.
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