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2018, CNS Neuroscience & Therapeutics. DOI: 10.1111/cns.13048
Machine learning in major depression: From classification to treatment outcome prediction
Shuang Gao, Vince D. Calhoun, Jing Sui
Abstract:
AbstractAimsMajor depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.DiscussionsIn this study, we review popular machine‐learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.ConclusionsWe hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
2024-02-29 21:11:00
#paper Machine learning in major depression: From classification to treatment outcome prediction doi 10.1111/cns.13048 这是篇综述机器学习在抑郁症脑影像数据中应用的文章,角度主要是分类和疗效预测。我们可以发现,大部分的此类研究用的都是小样本数据集,这就导致模型的泛化性有限。近年来,已经有越来越多的研究使用多中心大样本抑郁症数据集,但是这些研究的模型准确率相应的会降低。如何对抑郁症进行分亚型,进行特征筛选,选择合适的机器学习乃至深度学习的模型,保证泛化性的同时提高准确率,是抑郁症判别和疗效预测研究未来的重要方向。
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