When someone commits suicide, their family and friends can be left with the heartbreaking and answerless question of what they could have done differently. Colin Walsh, data scientist at Vanderbilt University Medical Center, hopes his work in predicting suicide risk will give people the opportunity to ask “what can I do?” while there’s still a chance to intervene.
Walsh and his colleagues have created machine-learning algorithms that predict, with unnerving accuracy, the likelihood that a patient will attempt suicide. In trials, results have been 80-90% accurate when predicting whether someone will attempt suicide within the next two years, and 92% accurate in predicting whether someone will attempt suicide within the next week.
Walsh’s paper, published in Clinical Psychological Science in April, is just the first stage of the work. He’s now working to establish whether his algorithm is effective with a completely different data set from another hospital. And, once confidant that the model is sound, Walsh hopes to work with a larger team to establish a suitable method of intervening. He expects to have an intervention program in testing within the next two years. “I’d like to think it’ll be fairly quick, but fairly quick in health care tends to be in the order of months,” he adds.
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