Bilalpur Maneesh, Hinduja Saurabh, Cariola Laura A, Sheeber Lisa B, Allen Nick, Jeni László A, Morency Louis-Philippe, Cohn Jeffrey F
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, USA.
Department of Psychology, University of Pittsburgh, Pittsburgh, USA.
IEEE Trans Affect Comput. 2023 Jan;2023. doi: 10.1109/fg57933.2023.10042796. Epub 2023 Feb 16.
Depression is the most common psychological disorder, a leading cause of disability world-wide, and a major contributor to inter-generational transmission of psychopathology within families. To contribute to our understanding of depression within families and to inform modality selection and feature reduction, it is critical to identify interpretable features in developmentally appropriate contexts. Mothers with and without depression were studied. Depression was defined as history of treatment for depression and elevations in current or recent symptoms. We explored two multimodal feature selection strategies in dyadic interaction tasks of mothers with their adolescent children for depression detection. Modalities included face and head dynamics, facial action units, speech-related behavior, and verbal features. The initial feature space was vast and inter-correlated (collinear). To reduce dimensionality and gain insight into the relative contribution of each modality and feature, we explored feature selection strategies using Variance Inflation Factor (VIF) and Shapley values. On an average collinearity correction through VIF resulted in about 4 times feature reduction across unimodal and multimodal features. Collinearity correction was also found to be an optimal intermediate step prior to Shapley analysis. Shapley feature selection following VIF yielded best performance. The top 15 features obtained through Shapley achieved 78% accuracy. The most informative features came from all four modalities sampled, which supports the importance of multimodal feature selection.
抑郁症是最常见的心理障碍，是全球致残的主要原因，也是家庭中心理病理学代际传播的主要促成因素。为了增进我们对家庭中抑郁症的理解，并为模式选择和特征约简提供信息，在适合发展阶段的背景下识别可解释的特征至关重要。我们对患有抑郁症和未患抑郁症的母亲进行了研究。抑郁症被定义为有抑郁症治疗史以及当前或近期症状有所加重。我们在患有抑郁症的母亲与其青春期子女的二元互动任务中探索了两种多模态特征选择策略，用于抑郁症检测。这些模式包括面部和头部动态、面部动作单元、言语相关行为以及语言特征。初始特征空间庞大且相互关联（共线性）。为了降低维度并深入了解每种模式和特征的相对贡献，我们使用方差膨胀因子（VIF）和沙普利值探索了特征选择策略。平均而言，通过VIF进行共线性校正导致单模态和多模态特征减少了约4倍。共线性校正也被发现是沙普利分析之前的最佳中间步骤。VIF之后的沙普利特征选择产生了最佳性能。通过沙普利获得的前15个特征达到了78%的准确率。最具信息量的特征来自所采样的所有四种模式，这支持了多模态特征选择的重要性。
