A recurring reproduction error in the administration of the Generalized Anxiety Disorder scale

Zorowitz, Samuel; Bennett, Daniel; Choe, Gina; Niv, Yaron

doi:10.1016/s2215-0366(21)00001-8

Cited by 3 publications

(1 citation statement)

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“…In actual treatments, due to the diverse manifestations of anxiety disorders, unpredictable courses, and variable treatment responses, their detection, diagnosis, and management often pose challenges to clinicians. When grading and assessing anxiety disorders, patients’ sensory perceptions and expressions vary, making it difficult to objectively and intuitively assess, inevitably leading to the problem of inaccurate grading assessments [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach

Luo,

Zhou,

Fang

et al. 2024

Diagnostics

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Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD.

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Section: Introductionmentioning

confidence: 99%