基于shap的母亲抑郁史预测了解对儿童行为的影响

Maneesh Bilalpur, Saurabh Hinduja, Laura Cariola, Lisa Sheeber, Nicholas Allen, Louis-Philippe Morency, Jeffrey F. Cohn
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引用次数: 0

摘要

抑郁症会严重影响父母的行为。父母的抑郁也会强烈影响孩子的行为吗?为了研究这个问题,我们比较了73名抑郁母亲和75名非抑郁母亲与其青春期孩子之间的二元互动。家庭收入较低,84%是白人。儿童行为是通过由专家编码人员手工注释语言和非语言行为,以及面部表情、面部和头部动态、韵律、语言行为和语言学的多模态计算测量来测量的。对于这两组度量,我们使用了支持向量机。对于计算测量,我们使用SHapley加性解释(SHAP)的新方法研究了单一与多种模式的相对贡献。计算方法优于人类专家的手动评级。在个体计算测量中,韵律是最具信息量的。SHAP减少导致特征数量减少了四倍,性能最高(准确率为77%;正面和负面协议分别占75%和76%)。这些发现表明,母亲抑郁对青春期儿童的行为有强烈的影响;差异主要体现在韵律上;多模态特征加上SHAP还原是最强大的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SHAP-based Prediction of Mother's History of Depression to Understand the Influence on Child Behavior
Depression strongly impacts parents’ behavior. Does parents’ depression strongly affect the behavior of their children as well? To investigate this question, we compared dyadic interactions between 73 depressed and 75 non-depressed mothers and their adolescent child. Families were of low income and 84% were white. Child behavior was measured from audio-video recordings using manual annotation of verbal and nonverbal behavior by expert coders and by multimodal computational measures of facial expression, face and head dynamics, prosody, speech behavior, and linguistics. For both sets of measures, we used Support Vector Machines. For computational measures, we investigated the relative contribution of single versus multiple modalities using a novel approach to SHapley Additive exPlanations (SHAP). Computational measures outperformed manual ratings by human experts. Among individual computational measures, prosody was the most informative. SHAP reduction resulted in a four-fold decrease in the number of features and highest performance (77% accuracy; positive and negative agreements at 75% and 76%, respectively). These findings suggest that maternal depression strongly impacts the behavior of adolescent children; differences are most revealed in prosody; multimodal features together with SHAP reduction are most powerful.
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