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    AI Helps Uncover Long-Term Effects of Antidepressants

    When NYU Shanghai Assistant Professor of Information Systems and Business Analytics Bruno Abrahao began studying artificial intelligence (AI), he never envisioned that his work might shape the world of psychiatric medicine. That changed after Abrahao served as a teaching assistant at a top research university in the United States. He discovered that many of his students’ academic issues were rooted in something deeper: depression and anxiety. “I had students reaching out to me, saying, ‘I’m very depressed, I can’t study, I can’t concentrate,’ or even worse, ‘I took out a loan to come here, and I’m not getting good grades. I can’t go back to my country with these grades, so I'm really anxious and I don’t know what to do.’” Although Abrahao helped the students get the psychiatric treatment they needed, he often felt frustrated by the mixed results of the medications students received. “Sometimes they got worse when they started taking medication,” said Abrahao. “So I became interested in using AI to illuminate this issue.” Abrahao and fellow researchers from Georgia Tech, Microsoft Research, and Harvard Medical School have developed an AI-based method to enable mental health practitioners to understand the long-term effects of psychiatric medications, as well as their effects across populations that are too big and too varied for clinical trials to evaluate. Their research was published in the June 14 issue of the Proceedings of the Association for the Advancement of Artificial Intelligence, and won the Outstanding Paper Award  at the  AAAI Conference on Artificial Intelligence. Adapting a machine learning model originally developed for Reddit posts, the team pinpointed over 23,000 Twitter users whose posts indicated that they were taking any one of 49 popular psychiatric medications and estimated when they began and ended their treatment. Next, the model found a control group of 280,000 Twitterers who used depressive language and who had similar social media habits, but who were not taking psychiatric medications. Using natural language processing techniques and observations of users’ posting behaviors, the researchers analyzed changes in mood, cognition, and thoughts of suicide in both groups in order to track the effects of psychiatric treatment. The results are striking: posts by Twitter users who took a family of drugs called SSRIs - which includes many of the most popular antidepressants - showed long-term worsening of symptoms including anxiety, depression, psychosis, and suicidal ideation. In contrast, the posts of users who took an older group of drugs called TCAs showed more improvement in depressive symptoms over the course of the study’s two-year term. Although psychiatrists and patients have expressed doubts about the effectiveness of SSRIs for years, Abrahao and his colleagues argue that their research substantiates the severity and scale of these negative effects in a large population. Ultimately, the team’s research provides convincing evidence that psychiatrists, as well as the pharmaceutical industry, can look to social media to evaluate patients’ treatment experience on a scale unimaginable even a few years ago, Arabhao says. “Our collaborator John Torous [Instructor of Psychiatry at Harvard Medical School] found our results reassuring, as these results reflect what he observed in his clinical experience,” Abrahao recalls. “At the same time, we need to complement our methodology with clinical patient data to link what we learn from social media to clinical implications.” The researchers hope to validate their findings by contacting and interviewing many of the individual Twitter users whose posts they tracked. Abrahao says he is confident that his group’s research have the potential to change best practices and regulations in psychiatric medicine. “Sometimes doctors benefit from a second opinion. That opinion can come from an algorithm,” says Abrahao. “It shouldn’t replace their judgement, but AI-assisted medicine is something that could be used today.”

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  • Members

    Members

    NYU Shanghai CBER’s faculty members are well-established experts in the field of marketing, economics, finance, accounting, and management, they strive to integrate a wealth of real-world experience and business acumen into academic and research activities.

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  • About Us

    About Us

    Located in the heart of Lujiazui, Shanghai’s leading financial center, NYU Shanghai Center for Business Education and Research (CBER) aims to promote innovative research on China-related business and to inspire academic collaboration among industry leaders, business faculty, and students through a variety of co-curricular activities. A distinctive feature of CBER is a robust research environment, one that supports interdisciplinary studies in business, liberal arts, and sciences. It also provides a varied, multicultural learning environment that gives students a global edge and a unique competitive advantage. As some of CBER’s faculty members are well-established experts in the field of marketing, economics, and finance, they strive to integrate a wealth of real-world experience and business acumen into academic and research activities. 上海纽约大学商学教育与研究中心于2015年成立,是一个旨在为多方参与者建立促进教学和研究的创新品质、强化人文博雅教育相对高等商学教育重要性的资源分享平台。 上海纽约大学的独特定位赋予了中心具备推动创新型商学教育的潜力、为商学理念开展求索性的工作,并积极探索结合文理教育和传统商学教育的教研内容与模式。我们欢迎包括高校师生、教育家与学者、业界领袖和精英,以及公共政策制定机构在内的多方参与者以中心所提供的平台,就创新型商学教育开展富有建设性的讨论和对话。  

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