Yusen Zhai

Assistant Professor University of Florida

  • Gainesville FL

Yusen Zhai’s research focuses on AI and machine learning and health policy to promote mental health outcomes and suicide prevention.

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Biography

Yusen Zhai is an assistant professor of counselor education. His research examines the impact of AI, health policy and social determinants on mental health, learning outcomes and disparities. He develops machine learning–based predictive models to identify risks for mental health conditions and suicide, with the aim of improving early detection and support. His work emphasizes data-driven approaches to prevention and intervention that inform both counseling practice and policy. In addition, Yusen studies how AI can transform clinical practice, counselor training and education to advance innovative solutions in mental health care and delivery.

Areas of Expertise

Predictive Modeling
Health Policy
Mental Health
Artificial Intelligence
Machine Learning in Health Research
Suicide Prevention
Health Disparities

Media Appearances

Is your college student at risk for anxiety? New UAB AI model can tell

AL.com  online

2025-02-12

Researchers from the University of Alabama at Birmingham’s School of Education and Human Sciences have developed a tool to assist counselors in identifying college students at heightened risk of anxiety and depressive disorders — and offering proactive solutions. This AI model was created to confront health disparities and educational inequities, amid a rise in mental health issues reported among American college students.

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COVID-19 vaccine rollout phases linked to decreased anxiety and depression in US adults

News Medical  online

2024-08-14

In a recent study published in The Lancet Regional Health – Americas, researchers evaluated whether the various coronavirus disease 2019 (COVID-19) vaccine rollout phases impacted the prevalence of depression and anxiety at the population level among adults in the country.

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PTSD Has Surged Among College Students

The New York Times  print

2024-05-30

Post-traumatic stress disorder diagnoses among college students more than doubled between 2017 and 2022, climbing most sharply as the coronavirus pandemic shut down campuses and upended young adults’ lives, according to new research.

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Articles

Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students

Journal of Counseling & Development

Zhai, et al.

2024-10-21

College student mental health has been a critical concern for professional counselors. Anxiety and depressive disorders have become increasingly prevalent over the past decade. Utilizing machine learning, a subset of artificial intelligence (AI), we developed predictive models (i.e., eXtreme Gradient Boosting [XGBoost], Random Forest, Decision Tree, and Logistic Regression) to identify US college students at heightened risk of diagnosable anxiety and depressive disorders.

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Impact of phased COVID-19 vaccine rollout on anxiety and depression among US adult population, January 2019–February 2023: a population-based interrupted time series analysis

The Lancet Regional Health - Americas

Zhai, et al.

2024-09-01

Existing research lacks information on the potential impacts of multi-phased coronavirus disease 2019 (COVID-19) vaccine rollouts on population mental health. This study aims to evaluate the impact of various COVID-19 vaccine rollout phases on trends and prevalence of anxiety and depression among US adults at a population level.

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Trends in Diagnosed Posttraumatic Stress Disorder and Acute Stress Disorder in US College Students, 2017-2022

JAMA Network Open

Zhai & Du

2024-05-30

We aimed to assess trends in prevalence of diagnosed PTSD and ASD among US college students from 2017 to 2022, a period marked by escalated societal stressors and global health crises. Understanding these trends is crucial for the development of targeted, trauma-informed intervention strategies to address the urgent needs of this population during a critical developmental stage.

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