Using AI for Drowning Prevention

Interns will use Artificial Intelligence and natural language processing to abstract and classify fatal drowning circumstances and risk factors from news articles. Drowning claims over 4,000 lives annually in the U.S., but the specific risk factors are often underexplored due to time-intensive manual data abstraction. This project aims to determine the feasibility of automating this process.

The intern will create a script that state health departments can use to abstract drowning data from regional news reports. They will also produce a report summarizing the feasibility and insights gained from using machine learning in this context. This role offers experience at the intersection of public health, data science, and innovation.