An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments
This work addresses the challenge of accurate suicide risk prediction for mental health patients, offering an incremental improvement over existing methods by leveraging administrative data with high dimensionality and redundancies.
The study tackled the problem of predicting short and medium-term suicide risk in mental health patients by evaluating randomized machine learning methods like random forests, gradient boosting machines, and deep neural nets with dropout against traditional approaches, finding that these methods showed robustness against data redundancies and superior performance on AUC and F-measure metrics.
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.