CDC <\/p>\n
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 <\/p>\n
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. <\/p>\n
\"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.\" <\/p>\n
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. <\/p>\n
Related: <\/p>\n
But not everyone who commits suicide has an extensive medical record or has ever even walked into a hospital. So another research team is taking a similar AI-powered approach to suicide prediction by examining data from a more ubiquitous source: smartphones. The DARPA-funded company Cogito has developed a mobile app called Companion that automatically gathers data on someone's communication and movement patterns. <\/p>\n
\"We don't listen to the conversations, but look at things like how many calls you make, how many miscalls you have,\" says Skyler Place, a scientist at Cogito. \"We are taking hundreds and thousands of data points, combining them in a way that humans can't.\" <\/p>\n
The data is then used to create a risk score that is shown to the clinician. If that score changes over time, the clinician calls the individual to check in and see whether they need additional care. Cogito is currently working with the Department of Veterans Affairs at a suicide prevention center in Denver, Colorado to test the app with veterans who are at high risk for suicide. If the tests are successful, Place imagines more widespread use in a couple of years. <\/p>\n
While an AI-powered tool may determine who is at risk of suicide, it cannot predict exactly when they might attempt it. Facebook is trying to solve this problem by <\/p>\n
CDC <\/p>\n
What happens after someone is found to be at risk is another challenge. Prevention strategies aren't always effective and it can be more difficult to protect high-risk individuals after they leave healthcare settings. To that end, researchers at the University of Vermont and Dartmouth have developed a system for both risk assessment and prevention. <\/p>\n
First, the patients use <\/p>\n
\"They wouldn't have been caught if it were not for this tool,\" says Bill J. Hudenko, assistant professor of psychiatry at Dartmouth's Geisel School of Medicine. <\/p>\n
The risk assessment tool is paired with <\/p>\n
If a patient is classified as high risk, a nurse will help them get set up with the Proxi app to create a support system of friends and family, Hudenko adds. \"Because we know what is most valuable in suicide prevention is to help them keep connections with others.\" <\/p>\n
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AI May Hold the Key to Stopping Suicide - NBCNews.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" CDC 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 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. Continue reading