The study offers a fascinating finding: machine learning — a future frontier for artificial intelligence — can predict with 80-90 percent accuracy whether someone will attempt suicide as far off as two years into the future. The algorithms become even more accurate as a person’s suicide attempt gets closer. For example, the accuracy climbs to 92 percent one week before a suicide attempt when artificial intelligence focuses on general hospital patients.
Ribeiro’s project was born out of Franklin’s startling findings. She and Franklin, along with Colin Walsh of Vanderbilt University Medical Center, successfully accessed a massive data repository containing the electronic health records of about 2 million patients in Tennessee. The project was the largest research study of its kind in history, a “huge opportunity,” Ribeiro said. The team combed through the electronic health records, which were anonymous, and identified more than 3,200 people who had attempted suicide.
Having that information was crucial; it contained detailed medical histories of thousands of people leading up to their suicide attempts. Using machine learning to examine all of those details, the algorithms were able to “learn” which combination of factors in the records could most accurately predict future suicide attempts.
It’s absolutely impossible to quantify how many little ways machine learning is going to be able to impact our lives. Since the advent of the electronic spreadsheet, the amount of data being stored has simply been growing exponentially. Human’s could barely keep up with spreadsheets when they were handwritten, there’s simply no way for a human to work broadly on these sizes of datasets. Machines on the other hand love these gigantic repositories.