Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students
Abstract
The rate of depression in college students is rising, which is known to increase suicide risk, lower academic performance and double the likelihood of dropping out of school. Existing work on fnding relationships between passively sensed behavior and depression, as well as detecting depression, mainly derives relevant unimodal features from a single sensor. However, co-occurrence of values in multiple sensors may provide better features, because such features can describe behavior in context. We present a new method to extract contextually fltered features from passively collected, time-series mobile data via association rule mining. After calculating traditional unimodal features from the data, we extract rules that relate unimodal features to each other using association rule mining. We extract rules from each class separately (e.g., depression vs. nondepression). We introduce a new metric to select a subset of rules that distinguish between the two classes. From these rules, which capture the relationship between multiple unimodal features, we automatically extract contextually fltered features. These features are then fed into a traditional machine learning pipeline to detect the class of interest (in our case, depression), defned by whether a student has a high BDI-II score at the end of the semester. The behavior rules generated by our methods are highly interpretable representations of diferences between classes. Our best model uses contextually-fltered features to signifcantly outperform a standard model that uses only unimodal features, by an average of 9.7% across a variety of metrics. We further verifed the generalizability of our approach on a second dataset, and achieved very similar results. CCS Concepts: • Human-centered computing Ubiquitous and mobile computing; • Applied computing Life and medical sciences.
