Toward Automatic Risk Assessment to Support Suicide Prevention.
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Adamou M1,2, Antoniou G2, Greasidou E3, Lagani V3,4, Charonyktakis P3, Tsamardinos I2,3,5, Doyle M1.
- South West Yorkshire Partnership NHS Foundation Trust, Wakefield, UK.
- Department of Computer Science, University of Huddersfield, UK.
- Gnosis Data Analysis PC, Heraklion, Greece.
- Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia.
- Computer Science Department, University of Crete, Heraklion, Greece.
Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide risk has proven extremely difficult to assess for medical specialists, and traditional methodologies deployed have been ineffective. Advances in machine learning make it possible to attempt to predict suicide with the analysis of relevant data aiming to inform clinical practice.
We aimed to (a) test our artificial intelligence based, referral-centric methodology in the context of the National Health Service (NHS), (b) determine whether statistically relevant results can be derived from data related to previous suicides, and (c) develop ideas for various exploitation strategies.
The analysis used data of patients who died by suicide in the period 2013-2016 including both structured data and free-text medical notes, necessitating the deployment of state-of-the-art machine learning and text mining methods.
Sample size is a limiting factor for this study, along with the absence of non-suicide cases. Specific analytical solutions were adopted for addressing both issues.
RESULTS AND CONCLUSION:
The results of this pilot study indicate that machine learning shows promise for predicting 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.
automated machine learning; clinical data; risk assessment; suicide prevention; text mining
- PMID: 30474411