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.

Using Machine Learning and 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

Homeless child

HIV prevention among homeless youth

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.

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.

Previous work

Sze-chuan Suen

Milind Tambe

Bryan Wilder

Han Ching Ou

Dana Goldman

Eric Rice

Carl Castro

Anthony Fulginiti

Anamika Barman-Adhikari

Phebe Vayanos

Aida Rahmattalabi

Jackson Killian

Aditya Mate

The California HIV/AIDS Research Program (CHRP) logo

In Advances in Neural and Information Processing Systems. 2020. (NeurIPS-20)

Aditya Mate*, Jackson Killian*, Haifeng Xu, Andrew Perrault, Milind Tambe (*equal contribution)
Collapsing Bandits and Their Application to Public Health Interventions

In AAAI Fall Symposium

Ankit Bhardwaj*, Han Ching Ou*, Haipeng Chen, Shahin Jabbari, Milind Tambe, Rahul Panicker, and Alpan Raval.

 

Robust Lock-Down Optimization for COVID-19 Policy Guidance

Aniruddha Adiga, Lijing Wang, Adam Sadilek, Ashish Tendulkar, Srinivasan Venkatramanana, Anil Vullikantia, Gaurav Aggarwal, Alok Talekar, Xue Ben, Jiangzhuo Chen, Bryan Lewis, Samarth Swarup, Milind Tambe, Madhav Marathe

Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

Jackson A. Killian, Marie Charpignon, Bryan Wilder, Andrew Perrault, Milind Tambe, and Maimuna S. Majumder. (SSRN 5/12/2020.)

Evaluating COVID-19 Lockdown and Reopening Scenarios For Georgia, Florida, and Mississippi 

Aditya Mate, Jackson A. Killian, Bryan Wilder, Marie Charpignon, Ananya Awasthi, Milind Tambe, and Maimuna S. Majumder. (SSRN 4/13/2020.)

Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States [code]

 

Bryan Wilder, Marie Charpignon, Jackson A Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe, and Maimuna S. Majumder. (SSRN 4/1/2020.)

The Role Of Age Distribution And Family Structure On Covid-19 Dynamics:A Preliminary Modeling Assessment For Hubei And Lombardy [code]

In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-20)

Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, Alpan Raval, and Milind Tambe
Who and When to Screen Multi-Round Active Screening for Network Recurrent Infectious Diseases Under Uncertainty

The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19)

Jackson A. Killian, Bryan Wilder, Amit Sharma, Vinod Choudhary, Bistra Dilkina, & Milind Tambe
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

Journal of the Society for Social Work and Research, Volume 9, Number 4. (November 2018).

Eric Rice, Amanda Yoshioka-Maxwell, Robin Petering, Laura Onasch-Vera, Jaih Craddock, Milind Tambe, Amulya Yadav, Bryan Wilder, Darlene Woo, Hailey Winetrobe, & Nicole Wilson
Piloting the Use of Artificial Intelligence to Enhance HIV Prevention Interventions for Youth Experiencing Homelessness

International Joint Conference on Artificial Intelligence (IJCAI), 2018

Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, Darlene Woo
Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth

International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18), 2018

Amulya Yadav, Ritesh Noothigattu, Eric Rice, Laura Onasch-Vera, Leandro Marcolino, Milind Tambe
Please be an influencer? Contingency Aware Influence Maximization

International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18), 2018

Bryan Wilder, Laura Onasch-Vera, Juliana Hudson, Jose Luna, Nicole Wilson, Robin Petering, Darlene Woo, Milind Tambe, Eric Rice
End-to-End Influence Maximization in the Field

International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18), 2018

Lily Hu, Bryan Wilder, Amulya Yadav, Eric Rice, Milind Tambe
Activating the “Breakfast Club”: Modeling Influence Spread in Natural-World Social Networks

International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18), 2018

Bryan Wilder, Han Ching Ou, Kayla de la Haye, Milind Tambe
Optimizing network structure for preventative health

Proceedings of the Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2015

Amulya Yadav, Leandro Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, Heather Carmichael
Preventing HIV Spread in Homeless Populations using PSINET

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016

Amulya Yadav, Hau Chan, Albert Jiang, Haifeng Xu, Eric Rice, Milind Tambe
Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization Under Uncertainty

Proceedings of the IDEAS Workshop in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016

Amulya Yadav, Hau Chan , Albert Jiang , Eric Rice, Ece Kamar, Barbara Grosz, Milind Tambe
POMDPs for Assisting Homeless Shelters – Computational and Deployment Challenges

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016

Leandro Marcolino, Aravind Laskhminarayanan, Amulya Yadav, Milind Tambe
Simultaneous Influencing and Mapping Social Networks (Extended Abstract)

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016

Amulya Yadav, Ece Kamar, Barbara Grosz, Milind Tambe
HEALER: POMDP Planning for Scheduling Interventions among Homeless Youth (Demonstration)

Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), 2017

Amulya Yadav, Bryan Wilder, Robin Petering, Eric Rice, Milind Tambe
Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application

Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), 2017

Bryan Wilder, Amulya Yadav, Nicole Immorlica, Eric Rice, Milind Tambe
Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network

International Joint Conference on Artificial Intelligence (IJCAI), 2017

Amulya Yadav, Hau Chan, Albert Xin Jiang, Haifeng Xu, Eric Rice, Milind Tambe
Maximizing Awareness about HIV in Social Networks of Homeless Youth with Limited Information

3rd International Workshop on Social Influence Analysis, 2017

Amulya Yadav, Aida Rahmattalabi, Ece Kamar, Phebe Vayanos, Milind Tambe, Venil Loyd Noronha
Explanations Systems for Influential Maximizations Algorithms

AAAI conference on Artificial Intelligence (AAAI-18), 2018

Bryan Wilder, Nicole Immorlica, Eric Rice, Milind Tambe
Maximizing Influence in an Unknown Social Network

AAAI Student Abstract Section (AAAI-18), 2018

Aida Rahmattalabi, Anamika Barman Adhikari, Phebe Vayanos, Milind Tambe, Eric Rice, Robin Baker
Influence Maximization for Social Network Based Substance Abuse Prevention