Automatic depression screening using social interaction data on smartphones

Depression is a serious and prevalent mental illness. The ubiquitous adoption of smartphones have enabled new opportunities for depression screening. Recently studies have used physical location and activity information automatically collected on smartphones for depression prediction. Social interactions also play a vital role in the overall health and well-being of individuals. In this work, we explore the feasibility of using social interaction data, specifically SMS and phone call logs, collected on smartphones for predicting depression. We extract a comprehensive set of features from such data. In addition, we construct a family of machine learning models by using these features for depression prediction. Using the social interaction data collected via an Android phone app from college-age students, we compare the characteristics of SMS and phone call usage patterns between depressed and non-depressed participants. We find that they exhibit more distinguishing behaviors in outgoing SMS messages and phone calls, which are initiated by the users, than incoming SMS messages and phone calls. Our results also demonstrate that social interaction data can be used to predict depression effectively, with F1 score as high as 0.82.

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