Using Data Science and AI to Advance Violence and Injury Prevention

Presentation Time: 08:30 AM - 12:00 PM

Abstract Keywords: Data Mining, Deep Learning, Machine Learning, Natural Language Processing, Data Standards, Legal, Ethical, Social and Regulatory Issues, Population Health
Primary Track: Applications
Programmatic Theme: Clinical Informatics

This 3-hour Collaborative Workshop will provide an overview of challenges and opportunities to violence and injury prevention and how clinical information systems and secondary data sources can be used to help identify individuals at risk. There will be a panel discussion discussing challenges and opportunities, mining state violent death data, using natural language processing to identify violence and injury variables, and the use of explainable AI, followed by poster presentations and discussion. The workshop will conclude with subgroup discussion sections focusing on 1) Clinical and public health use cases, 2)Legal and policy issues, 3)Statistical analyses and methods, and 4) AI methods for violence and injury prevention. Summary discussion will include all participants and how these methods and data sources could be applied to their research projects.

Speaker(s):
T. Elizabeth Workman, PhD
The George Washington University

Qing Zeng, PhD
George Washington University

Cynthia Brandt, MD, MPH
Yale University, School of Medicine

Lori Post, PhD
Northwestern University, Feinberg School of Medicine

Suzanne Tamang, PhD
Stanford University

Author(s):