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$2.5 Million NIH Grant Will Support AI Approach to Study and Predict Excessive Drinking

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A unique data-driven scientific approach to study and predict excessive drinking using social media and mobile-phone data has won Andrew Schwartz, Assistant Professor in the Department of Computer Science, and his team a $2.5M award from the National Institutes of Health (NIH). Their research will develop an innovative approach utilizing data from texting and social media as well as mobile phone apps to better understand how unhealthy drinking manifests in daily life and push the envelope for the ability of Artificial Intelligence (AI) to predict human behaviors.

The cross-disciplinary team will build AI models that predict future drinking as well as future effects of drinking on an individual’s mood among service industry workers. The award will be distributed over four years in collaboration with Richard Rosenthal, MD, Director of Addiction Psychiatry at Stony Brook Medicine, and Christine DeLorenzo, Associate Professor in the Departments of Biomedical Engineering and Psychiatry and a team at the University of Pennsylvania.

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Diagram illustrates AI initiative to track excessive drinking.

Diagram illustrates AI initiative to track excessive drinking.

“Andy is blazing the trail in advancing AI tools for tackling major health challenges,” said Fotis Sotiropoulos, Dean, College of Engineering and Applied Sciences. “His work is an ingenious approach using data-science tools, smart-phones and social media postings to identify early signs of alcohol abuse and alcoholism and guide interventions. This is AI-driven discovery and innovation at its very best!”

With the aid of the team’s methods, social media content can be collected and analyzed faster and cheaper, and present an unscripted, less biased psychological assessment. Traditional ecological studies of unhealthy drinking are done via costly and time-consuming phone interviews, which can also be subject to poor data quality and biases. Schwartz and his team will use their novel AI-based approach over social media text content. Samir Das, Chair of the Department of Computer Science, said: “Andrew has very successfully applied large-scale data and text analysis techniques for important and timely human health and well-being applications with very impactful results.” 

“We now know analysis of everyday language can cover a wide array of daily factors affecting individual health but their use over timespans is limited. The methods we will develop in this project should enable real-time study into how health plays out in the each individuals’ own words and bring about the possibility for personalized mental health care,” said Schwartz. 

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Dr. Schwartz’s research team confers online.

The technology will be developed with a focus on a population of frontline restaurant workers — bartenders and servers — a group that has among the highest rates of heavy alcohol use of all professions. This unhealthy drinking (defined as seven drinks a week for women and 14 for men, according to the National Institute on Alcohol Abuse and Alcoholism) creates the potential for extensive negative consequences related to work performance, relationships, and physical and psychological health. For example, the team will look at the effect of empathy, as measured through language. Psychologists on the team note that empathy can be both health-promoting (beneficial) and health-threatening (depleting). Distinguishing beneficial versus depleting empathy is an example where AI can capture something difficult to get at through questions. It’s also a dimension of human psychology suspected to play a role in stress on servers and bartenders since they often listen to customers’ problems and provide advice, which could have a negative effect on them.

The team’s research will include development of:

  • Temporal methods to automatically assess the association between unhealthy alcohol consumption and affect and empathy among restaurant industry workers based on their linguistic behavior in social media
  • A mobile app for longitudinal tracking of drinking behavior and psychological health, which will use data collected with it to build predictive models
  • Methods to assess the relationship of community factors with unhealthy drinking, utilizing AI-based representations of the communities in which restaurant industry workers live and work

Schwartz’s research has also focused on the impact of social media to predict mental and physical health issues. He is using Twitter to study COVID-19 and before that he focused on depression and social media posts

About the Researcher
Andrew Schwartz
 is an Assistant Professor in the Department of Computer Science and a faculty member of the Institute for AI-Driven Discovery and Innovation. His research utilizes natural language processing and machine learning techniques to focus on large-scale language analysis for health and social sciences.

— Dan Olawski

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