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An integrated framework for suicide risk prediction

Version 2 2024-05-30, 15:29
Version 1 2015-03-17, 16:05
conference contribution
posted on 2024-05-30, 15:29 authored by Truyen TranTruyen Tran, QD 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.

History

Volume

Part F128815

Pagination

1410-1418

Location

Chicago, Ill.

Start date

2013-08-11

End date

2013-08-14

ISBN-13

9781450321747

Language

eng

Publication classification

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

Copyright notice

2013, ACM

Editor/Contributor(s)

Dhillon IS, Koren Y, Ghani R, Senator TE, Bradley P, Parekh R, He J, Grossman RL, Uthurusamy R

Title of proceedings

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

Event

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

Publisher

Association for Computing Machinery (ACM)

Place of publication

New York, N.Y.