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Depression screening using a non-verbal self-association task: A machine-learning based pilot study
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DOI：10.1016/j.jad.2022.04.122.png)
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Background: Effective screening is important to combat the raising burden of depression and opens a critical time window for early intervention. Clinical use of non-verbal depression screening is nascent, yet a promising and viable candidate to supplement verbal screening. Differential self-and emotion-processing in depression patients were previously reported by non-verbal behavioural assessments, corroborated by neuroimaging findings of distinct neuroanatomical markers. Thus non-verbal validated brain-behaviour based self-emotion-related assessment data reflect physiological differences and may support individual level screening of depression.\ Methods: In this pilot study (n = 84) we collected two longitudinal sessions of behavioural assessment data in a laboratory setting. Depression was assessed using Beck Depression Inventory II (BDI-II), to explore optimal screening methods with machine-learning, and to establish the validity of adapting a novel behavioural assessment focusing on self and emotions for depression screening. Results: The best machine-learning model achieved high performance in depression screening, 10-Fold cross validation (CV) Area Under the receiver operating characteristic Curve (AUC) of 0.90 and balanced accuracy of 0.81, using a Gradient Boosting algorithm. Prospective prediction using a model trained with session 1 data to predict session 2 depression status achieved a 10-Fold CV AUC of 0.77 and balanced accuracy of 0.66. We also identified interpretable behavioural signatures for depression patients based on the best model. Conclusion: The study supports the utility of using behavioural data as a viable and cost-effective solution for depression screening, with a potential wide range of applications in clinical settings.
Keyword:
Depression
Self
Machine-learning
Matching technique
Sensitive objective measurement
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2.1W
被引数:
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