萧条(经济学)
脑电图
精神科
鉴定(生物学)
临床诊断
医学
心理学
临床心理学
植物
生物
经济
宏观经济学
作者
Yuan Liu,Changqin Pu,Xia Shan,Dingyu Deng,Xing Wang,Mengqian Li
标识
DOI:10.1515/tnsci-2022-0234
摘要
Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.
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