Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous data set from the University of Notre Dame's NetHealth study that collected individuals' (student participants') social interaction data via smartphones, health-related behavioral data via wearables (Fitbit), and trait data from surveys. To integrate the different types of information, we model the NetHealth data as a heterogeneous information network (HIN). Then, we redefine the problem of predicting individuals' mental health conditions (depression or anxiety) in a novel manner, as applying to our HIN a popular paradigm of a recommender system (RS), which is typically used to predict the preference that a person would give to an item (e.g., a movie or book). In our case, the items are the individuals' different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals' mental health as another problem type - that of node classification (NC) in our HIN, evaluating in the process four node features under logistic regression as a proof-of-concept classifier. We find that our RS and NC network methods produce more accurate predictions than a logistic regression model using the same NetHealth data in the traditional non-network fashion as well as a random-approach. Also, we find that the best of the considered RS approaches outperforms all considered NC approaches. This is the first study to integrate smartphone, wearable sensor, and survey data in an HIN manner and use RS or NC on the HIN to predict individuals' mental health conditions.

中文翻译：

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预测个体心理健康的异构网络方法

抑郁和焦虑是影响全球数百万人的重要公共卫生问题。为了识别易患抑郁症和焦虑症的个体，已经建立了通常利用来自一个来源的数据的预测模型。与这些传统模型不同，在本研究中，我们利用了圣母大学 NetHealth 研究中丰富的异构数据集，该研究通过智能手机收集了个人（学生参与者）的社交互动数据，通过可穿戴设备 (Fitbit) 收集了与健康相关的行为数据，和来自调查的特征数据。为了整合不同类型的信息，我们将 NetHealth 数据建模为异构信息网络 (HIN)。然后，我们以一种新颖的方式重新定义了预测个人心理健康状况（抑郁或焦虑）的问题，将推荐系统 (RS) 的流行范式应用于我们的 HIN，该范式通常用于预测一个人对某个项目（例如，电影或书籍）的偏好。在我们的案例中，项目是个人不同的心理健康状态。我们评估了四种最先进的 RS 方法。此外，我们将个人心理健康的预测建模为另一种问题类型——我们 HIN 中的节点分类 (NC)，在此过程中评估逻辑回归下的四个节点特征作为概念验证分类器。我们发现我们的 RS 和 NC 网络方法比使用传统非网络方式和随机方法使用相同 NetHealth 数据的逻辑回归模型产生更准确的预测。还，我们发现所考虑的最佳 RS 方法优于所有考虑的 NC 方法。这是第一项以 HIN 方式集成智能手机、可穿戴传感器和调查数据，并在 HIN 上使用 RS 或 NC 来预测个人心理健康状况的研究。