Olivia Goldhill, a writer who focuses on philosophy and psychology, has written an interesting post on a paper published by Colin Walsh, a data scientist at the Vanderbilt University Medical Center, co-authored with Jessica D Ribeiro and Joseph C Franklin.
Olivia writes:
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.
The prediction is based on data that’s widely available from all hospital admissions, including age, gender, zip codes, medications, and prior diagnoses. Walsh and his team gathered data on 5,167 patients from Vanderbilt University Medical Center that had been admitted with signs of self-harm or suicidal ideation. They read each of these cases to identify the 3,250 instances of suicide attempts.
Please do read Olivia’s piece further about important questions related to role of computers in such sensitive matters and the complexity of such algorithms.
The paper is published in the Sage Journal Clinical Psychological Science in April (Vol 5, Issue 3, 2017) and can be accessed from here. The authors write:
We developed machine learning algorithms that accurately predicted future suicide attempts (AUC = 0.84, precision = 0.79, recall = 0.95, Brier score = 0.14). Moreover, accuracy improved from 720 days to 7 days before the suicide attempt, and predictor importance shifted across time. These findings represent a step toward accurate and scalable risk detection and provide insight into how suicide attempt risk shifts over time.
I am guessing that ensemble methods were used for modeling the algorithms and that Deep Learning has not been used. I have reached out to the authors and am awaiting their response.
