Originally posted May 23, 2017
Here is an excerpt:
So far the results are promising. Using AI, Ribeiro and her colleagues were able to predict whether someone would attempt suicide within the next two years at about 80 percent accuracy, and within the next week at 92 percent accuracy. Their findings were recently reported in the journal Clinical Psychological Science.
This high level of accuracy was possible because of machine learning, as researchers trained an algorithm by feeding it anonymous health records from 3,200 people who had attempted suicide. The algorithm learns patterns through examining combinations of factors that lead to suicide, from medication use to the number of ER visits over many years. Bizarre factors may pop up as related to suicide, such as acetaminophen use a year prior to an attempt, but that doesn't mean taking acetaminophen can be isolated as a risk factor for suicide.
"As humans, we want to understand what to look for," Ribeiro says. "But this is like asking what's the most important brush stroke in a painting."
With funding from the Department of Defense, Ribeiro aims to create a tool that can be used in clinics and emergency rooms to better find and help high-risk individuals.
The article is here.