  Accurate prediction of suicide risk in mental health patients remains an open
problem. Existing methods including clinician judgments have acceptable
sensitivity, but yield many false positives. Exploiting administrative data has
a great potential, but the data has high dimensionality and redundancies in the
recording processes. We investigate the efficacy of three most effective
randomized machine learning techniques random forests, gradient boosting
machines, and deep neural nets with dropout in predicting suicide risk. Using a
cohort of mental health patients from a regional Australian hospital, we
compare the predictive performance with popular traditional approaches
clinician judgments based on a checklist, sparse logistic regression and
decision trees. The randomized methods demonstrated robustness against data
redundancies and superior predictive performance on AUC and F-measure.
