Abstract
Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.
Original language | English |
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Title of host publication | Proceedings - 10th Hellenic Conference on Artificial Intelligence, SETN 2018 |
Place of Publication | New York, New York, USA |
Publisher | Association for Computing Machinery (ACM) |
Number of pages | 8 |
ISBN (Electronic) | 9781450364331 |
ISBN (Print) | 9781450364331 |
DOIs | |
Publication status | Published - 9 Jul 2018 |
Event | 10th Hellenic Conference on Artificial Intelligence - Patras, Greece Duration: 9 Jul 2018 → 12 Jul 2018 Conference number: 10 http://setn2018.upatras.gr/ (Link to Conference Website) |
Conference
Conference | 10th Hellenic Conference on Artificial Intelligence |
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Abbreviated title | SETN 2018 |
Country/Territory | Greece |
City | Patras |
Period | 9/07/18 → 12/07/18 |
Internet address |
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