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Accurate prediction of suicide risk in mental health patients remains an openproblem. Existing methods including clinician judgments have acceptablesensitivity, but yield many false positives. Exploiting administrative data hasa great potential, but the data has high dimensionality and redundancies in therecording processes. We investigate the efficacy of three most effectiverandomized machine learning techniques random forests, gradient boostingmachines, and deep neural nets with dropout in predicting suicide risk. Using acohort of mental health patients from a regional Australian hospital, wecompare the predictive performance with popular traditional approachesclinician judgments based on a checklist, sparse logistic regression anddecision trees. The randomized methods demonstrated robustness against dataredundancies and superior predictive performance on AUC and F-measure.

An evaluation of randomized machine learning methods for redundant data:  Predicting short and medium-term suicide risk from administrative records and  risk assessments