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| Content Provider | ACM Digital Library |
|---|---|
| Author | Luo, Wei Harvey, Richard Phung, Dinh Tran, Truyen Venkatesh, Svetha Berk, Michael |
| Abstract | 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 $l_{1}-norm$ 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. |
| Starting Page | 1410 |
| Ending Page | 1418 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781450321747 |
| DOI | 10.1145/2487575.2488196 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-08-11 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Medical data analysis Risk modelling One-sided convolutional kernels Machine learning Risk prediction Filter bank Suicide |
| Content Type | Text |
| Resource Type | Article |
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