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

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

Amulya Yadav, Ece Kamar, Barbara Grosz, and Milind Tambe. 2016. “HEALER: POMDP Planning for Scheduling Interventions among Homeless Youth (Demonstration).” In International conference on Autonomous Agents and Multiagent Systems.Abstract
Adaptive software agents like HEALER have been proposed in the literature recently to recommend intervention plans to homeless shelter officials. However, generating networks for HEALER’s input is challenging. Moreover, HEALER’s solutions are often counter-intuitive to people. This demo paper makes two contributions. First, we demonstrate HEALER’s Facebook application, which parses the Facebook contact lists in order to construct an approximate social network for HEALER. Second, we present a software interface to run human subject experiments (HSE) to understand human biases in recommendation of intervention plans. We plan to use data collected from these HSEs to build an explanation system for HEALER’s solutions.
Amulya Yadav, Hau Chan, Albert Jiang, Eric Rice, Ece Kamar, Barbara Grosz, and Milind Tambe. 2016. “POMDPs for Assisting Homeless Shelters - Computational and Deployment Challenges.” In AAMAS 2016 IDEAS Workshop.Abstract
This paper looks at challenges faced during the ongoing deployment of HEALER, a POMDP based software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. In order to compute its plans, HEALER (i) casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; (ii) and constructs social networks of homeless youth at low cost, using a Facebook application. HEALER is currently being deployed in the real world in collaboration with a homeless shelter. Initial feedback from the shelter officials has been positive but they were surprised by the solutions generated by HEALER as these solutions are very counterintuitive. Therefore, there is a need to justify HEALER’s solutions in a way that mirrors the officials’ intuition. In this paper, we report on progress made towards HEALER’s deployment and detail first steps taken to tackle the issue of explaining HEALER’s solutions.
Leandro Soriano Marcolino, Aravind Lakshminarayanan, Amulya Yadav, and Milind Tambe. 2016. “Simultaneous Influencing and Mapping Social Networks (Extended Abstract).” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Abstract
Influencing a social network is an important technique, with potential to positively impact society, as we can modify the behavior of a community. For example, we can increase the overall health of a population; Yadav et al. (2015) [4], for instance, spread information about HIV prevention in homeless populations. However, although influence maximization has been extensively studied [2, 1], their main motivation is viral marketing, and hence they assume that the social network graph is fully known, generally taken from some social media network. However, the graphs recorded in social media do not really represent all the people and all the connections of a population. Most critically, when performing interventions in real life, we deal with large degrees of lack of knowledge. Normally the social agencies have to perform several interviews in order to learn the social network graph [3]. These highly unknown networks, however, are exactly the ones we need to influence in order to have a positive impact in the real world, beyond product advertisement. Additionally, learning a social network graph is very valuable per se. Agencies need data about a population, in order to perform future actions to enhance their well-being, and better actuate in their practices [3]. As mentioned, however, the works in influence maximization are currently ignoring this problem. Each person in a social network actually knows other people, including the ones she cannot directly influence. When we select someone for an intervention (to spread influence), we also have an opportunity to obtain knowledge. Therefore, in this work we present for the first time the problem of simultaneously influencing and mapping a social network. We study the performance of the classical influence maximization algorithm in this context, and show that it can be arbitrarily low. Hence, we study a class of algorithms for this problem, performing an experimentation using four real life networks of homeless populations. We show that our algorithm is competitive with previous approaches in terms of influence, and is significantly better in terms of mapping.
Amulya Yadav, Hau Chan, Albert Xin Jiang, Haifeng Xu, Eric Rice, and Milind Tambe. 2016. “Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2016.Abstract
This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address three real-world issues: (i) they completely fail to scale up to real-world sizes; (ii) they do not handle deviations in execution of intervention plans; (iii) constructing real-world social networks is an expensive process. HEALER handles these issues via four major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable realworld sizes; (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner; (iii) HEALER constructs social networks of homeless youth at low cost, using a Facebook application. Finally, (iv) we show hardness results for the problem that HEALER solves. HEALER will be deployed in the real world in early Spring 2016 and is currently undergoing testing at a homeless shelter.
Amulya Yadav, Aida Rahmattalabi, Ece Kamar, Phebe Vayanos, Milind Tambe, and Venil Loyd Noronha. 2017. “Explanations Systems for Influential Maximizations Algorithms.” In 3rd International Workshop on Social Influence Analysis.Abstract
The field of influence maximization (IM) has made rapid advances, resulting in many sophisticated algorithms for identifying “influential” members in social networks. However, in order to engender trust in IM algorithms, the rationale behind their choice of “influential” nodes needs to be explained to its users. This is a challenging open problem that needs to be solved before these algorithms can be deployed on a large scale. This paper attempts to tackle this open problem via four major contributions: (i) we propose a general paradigm for designing explanation systems for IM algorithms by exploiting the tradeoff between explanation accuracy and interpretability; our paradigm treats IM algorithms as black boxes, and is flexible enough to be used with any algorithm; (ii) we utilize this paradigm to build XplainIM, a suite of explanation systems; (iii) we illustrate the usability of XplainIM by explaining solutions of HEALER (a recent IM algorithm) among ∼200 human subjects on Amazon Mechanical Turk (AMT); and (iv) we provide extensive evaluation of our AMT results, which shows the effectiveness of XplainIM.
Amulya Yadav, Hau Chan, Albert Xin Jiang, Haifeng Xu, Eric Rice, and Milind Tambe. 2017. “Maximizing Awareness about HIV in Social Networks of Homeless Youth with Limited Information.” In International Joint Conference on Artificial Intelligence (IJCAI).Abstract
This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address two real-world issues: (i) they completely fail to scale up to real-world sizes; and (ii) they do not handle deviations in execution of intervention plans. HEALER handles these issues via two major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; and (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviationtolerant manner. HEALER was deployed in the real world in Spring 2016 with considerable success.
Bryan Wilder, Amulya Yadav, Nicole Immorlica, Eric Rice, and Milind Tambe. 2017. “Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network.” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).Abstract
This paper focuses on new challenges in influence maximization inspired by non-profits’ use of social networks to effect behavioral change in their target populations. Influence maximization is a multiagent problem where the challenge is to select the most influential agents from a population connected by a social network. Specifically, our work is motivated by the problem of spreading messages about HIV prevention among homeless youth using their social network. We show how to compute solutions which are provably close to optimal when the parameters of the influence process are unknown. We then extend our algorithm to a dynamic setting where information about the network is revealed at each stage. Simulation experiments using real world networks collected by the homeless shelter show the advantages of our approach.
Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, and Darlene Woo. 2017. “Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application.” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).Abstract
This paper focuses on a topic that is insufficiently addressed in the literature, i.e., challenges faced in transitioning agents from an emerging phase in the lab, to a deployed application in the field. Specifically, we focus on challenges faced in transitioning HEALER and DOSIM, two agents for social influence maximization, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, this paper illustrates that transitioning these agents from the lab into the real-world is not straightforward, and outlines three major lessons. First, it is important to conduct real-world pilot tests; indeed, due to the health-critical nature of the domain and complex influence spread models used by these agents, it is important to conduct field tests to ensure the real-world usability and effectiveness of these agents. We present results from three real-world pilot studies, involving 173 homeless youth in an American city. These are the first such pilot studies which provide headto-head comparison of different agents for social influence maximization, including a comparison with a baseline approach. Second, we present analyses of these real-world results, illustrating the strengths and weaknesses of different influence maximization approaches we compare. Third, we present research and deployment challenges revealed in conducting these pilot tests, and propose solutions to address them. These challenges and proposed solutions are instructive in assisting the transition of agents focused on social influence maximization from the emerging to the deployed application phase.