Suicide rates are currently derived from a national database of each state’s cause-of-death reports. While accurate, that information can take up to two years to compile, leading officials and government agencies to allot resources and draft policies based on somewhat outdated statistics.
The researchers are therefore using machine learning AI to analyze health data, such as that gathered by crisis text and call lines, and content from platforms such as Reddit and Twitter to formulate a more accurate forecast of the real-time suicide rate.
The first phase of the research resulted in “remarkable success,” with only about a 1 percent difference between the AI’s predicted rate and the actual suicide rate for a given year, Munmun De Choudhury, PhD, an assistant professor at Georgia Tech’s School of Interactive Computing who is part of the research team, told Recode.
“What our method does is give estimates at a weekly granularity over all of 2019,” Dr. De Choudhury explained. “What we are saying is that we can now estimate these rates of suicide up to a year in advance of when death records become available.”
Read more here.
More articles on AI:
Nuance, Microsoft expand availability of clinician note-taking AI
U of Waterloo develops AI software to detect early-stage breast cancer
Mayo Clinic AI enables EKGs to screen for hypertrophic cardiomyopathy
