We are living in a world of great technological advances, which is inevitably also linked to production of huge amounts of data. By using machine learning, the idea that a computer can form algorithms from the data that it is given, extensive data can be analyzed and interpreted efficiently. What was once seen only in science fiction movies, is now a reality. Machine leaning is a powerful technology used for various applications, from weather prediction to image recognition. But what if we could use the advantages of machine learning to better understand and perhaps even predict suicidal behavior?
The review paper published in BJBMS provides an overview of available literature, examining high-throughput studies tied to the biological component of suicidal behavior. Based on the methods used, studies were categorized into four distinct categories of gene and genome-based studies, metabolite-analysis-based studies, epigenome-based and transcriptome-based studies, and imaging-based studies.
As suicidal behavior remains a global problem debilitating millions of people, it is important to search for new directions to improve public health. Using machine learning approaches, researchers have shown that computer models are able to recognize and differentiate groups based on their biological traits and behavior.
While still in its infancy, the potential of machine learning is great. Once more data is collected and limitations are decreased, machine learning will be able to help design better therapies and treatment opportunities, and improve psychotherapy approaches.
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Editor: Edna Skopljak, MD
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