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

Authors

  • Alja Videtič Paska Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia https://orcid.org/0000-0002-1182-5417
  • Katarina Kouter Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia https://orcid.org/0000-0002-1051-959X

DOI:

https://doi.org/10.17305/bjbms.2020.5146

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

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Published

01-08-2021

How to Cite

1.
Machine learning as the new approach in understanding biomarkers of suicidal behavior. Biomol Biomed [Internet]. 2021 Aug. 1 [cited 2024 May 20];21(4):398-40. Available from: https://bjbms.org/ojs/index.php/bjbms/article/view/5146

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