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首页> 外文会议>2016 IEEE First Conference on Connected Health: Applications, Systems and Engineering Technologies >Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression
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Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression

机译：多视图双向聚类，以识别表示抑郁的智能手机感应功能

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摘要

Depression is a major public health issue with direct and significant effects on both physical and mental health. In this study, we analyze smartphone sensing data to find differential behavioral features that are correlated with depression measures such as patient health questionnaire (PHQ-9). Our approach uses an innovative multi-view bi-clustering algorithm. It takes multiple views of sensing data as input to identify homogeneous behavioral groups and simultaneously the key sensing features that characterize the different groups. Using a publicly available dataset, we discover that these behavioral groups with differential sensing features are highly discriminative of PHQ-9 scores that are self reported by the study subjects. For instance, the group comprising less active users in the sensed activities corresponds to overall higher PHQ-9 scores. We then employ the key sensing features that distinguish the different groups to create predictive models to predict the group assignment of individuals. We verify the generalizability of these models using the support vector machine classifier. Cross validation studies show that our classifiers can classify individuals into the correct subgroups with an overall accuracy of 87%.
机译：抑郁症是一个重大的公共卫生问题，对身心健康都有直接而重大的影响。在这项研究中，我们分析了智能手机的感应数据，以发现与抑郁测量（例如患者健康调查表（PH​​Q-9））相关的差异行为特征。我们的方法使用了创新的多视图双向聚类算法。它以感测数据的多个视图作为输入，以识别同质的行为组，并同时识别表征不同组的关键感测功能。使用公开可用的数据集，我们发现这些具有差异感测功能的行为群体高度区分了研究对象自行报告的PHQ-9得分。例如，在所感测的活动中包括较少活动用户的组对应于总体较高的PHQ-9得分。然后，我们采用区分不同群体的关键感知功能来创建预测模型，以预测个人的群体分配。我们使用支持向量机分类器验证了这些模型的可推广性。交叉验证研究表明，我们的分类器可以将个人分类为正确的亚组，整体准确性为87％。

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