An integrated framework for suicide risk prediction

Tran, Truyen, Phung, Dinh, Luo, Wei, Harvey, Richard, Berk, Michael and Venkatesh, Svetha 2013, An integrated framework for suicide risk prediction, in KDD'13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Association for Computing Machinery (ACM), New York, N.Y., pp. 1410-1418, doi: 10.1145/2487575.2488196.

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Title An integrated framework for suicide risk prediction
Author(s) Tran, TruyenORCID iD for Tran, Truyen
Phung, DinhORCID iD for Phung, Dinh
Luo, WeiORCID iD for Luo, Wei
Harvey, Richard
Berk, MichaelORCID iD for Berk, Michael
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Conference name ACM SIGKDD international conference on Knowledge discovery and data mining (19th : 2013 : Chicago, Ill.)
Conference location Chicago, Ill.
Conference dates 11-14 Aug. 2013
Title of proceedings KDD'13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Editor(s) Dhillon, I. S.
Koren, Y.
Ghani, R.
Senator, T. E.
Bradley, P.
Parekh, R.
He, J.
Grossman, R. L.
Uthurusamy, R.
Publication date 2013
Start page 1410
End page 1418
Total pages 9
Publisher Association for Computing Machinery (ACM)
Place of publication New York, N.Y.
Summary 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.
ISBN 9781450321747
Language eng
DOI 10.1145/2487575.2488196
Field of Research 080109 Pattern Recognition and Data Mining
111711 Health Information Systems (incl Surveillance)
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2013, ACM
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