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.
Using Machine Learning and Multi-Agent Planning to Fight Tuberculosis
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
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.
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.
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
Bryan Wilder
Han Ching Ou
Dana Goldman
Eric Rice
Carl Castro
Anthony Fulginiti
Anamika Barman-Adhikari
Phebe Vayanos
Aida Rahmattalabi
Jackson Killian
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.
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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 |