Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
Bosn J Basic Med Sci. 2021 Aug 1;21(4):398-408. doi: 10.17305/bjbms.2020.5146.
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.
在精神病学领域，与其他医学领域相比，识别能够补充当前临床访谈、实现更客观和更快临床诊断、准确监测治疗反应和缓解的生物标志物是至关重要的。当前的技术发展使得能够以合理的成本在高通量规模上分析各种生物标志物，因此“组学”研究正在进入精神病学研究领域。然而，大数据需要全新的一系列数据处理技能，才能提取出临床有用的信息。到目前为止，经典的数据分析方法并没有真正有助于确定精神病学中的生物标志物，但如果应用机器学习形式的人工智能，大量的数据可能会达到更高的水平。发表的关于精神病学中机器学习的研究并不多，但从为数不多的研究中，我们已经可以看到，有可能为包括自杀在内的不同精神病理学构建一个生物标志物筛选组合。
