Deakin University

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An integrated framework for suicide risk prediction

conference contribution
posted on 2013-01-01, 00:00 authored by Truyen TranTruyen Tran, Quoc-Dinh Phung, Wei LuoWei Luo, Richard HarveyRichard Harvey, Michael BerkMichael Berk, Svetha VenkateshSvetha Venkatesh
Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient’s clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in 1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.



ACM SIGKDD international conference on Knowledge discovery and data mining (19th : 2013 : Chicago, Ill.)


1410 - 1418


Association for Computing Machinery (ACM)


Chicago, Ill.

Place of publication

New York, N.Y.

Start date


End date






Publication classification

E Conference publication; E1.1 Full written paper - refereed

Copyright notice

2013, ACM


I Dhillon, Y Koren, R Ghani, T Senator, P Bradley, R Parekh, J He, R Grossman, R Uthurusamy

Title of proceedings

KDD'13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining