Machine learning as the new approach in understanding biomarkers of suicidal behavior

Keywords: Suicide, artificial intelligence, personalized medicine, precision medicine, precision psychiatry

Abstract

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore ‘omic’ studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

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Machine learning as the new approach to understand biomarkers of suicidal behavior
Published
2020-12-31
How to Cite
1.
Videtič Paska A, Kouter K. Machine learning as the new approach in understanding biomarkers of suicidal behavior. Bosn J of Basic Med Sci [Internet]. 2020Dec.31 [cited 2021Apr.19];. Available from: https://www.bjbms.org/ojs/index.php/bjbms/article/view/5146
Section
Reviews

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