Cross-platform Prediction of Depression Treatment Outcome Using Location Sensory Data on Smartphones
This work addresses the need for less burdensome and costly monitoring of depression treatment outcomes for patients and clinicians, though it is incremental in applying domain adaptation to a new domain.
The paper tackled the problem of predicting depression treatment outcomes by using passively collected smartphone location data, addressing platform heterogeneity with domain adaptation. The approach achieved an F1 score of up to 0.67, comparable to traditional questionnaire-based methods.
Currently, depression treatment relies on closely monitoring patients response to treatment and adjusting the treatment as needed. Using self-reported or physician-administrated questionnaires to monitor treatment response is, however, burdensome, costly and suffers from recall bias. In this paper, we explore using location sensory data collected passively on smartphones to predict treatment outcome. To address heterogeneous data collection on Android and iOS phones, the two predominant smartphone platforms, we explore using domain adaptation techniques to map their data to a common feature space, and then use the data jointly to train machine learning models. Our results show that this domain adaptation approach can lead to significantly better prediction than that with no domain adaptation. In addition, our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.67, comparable to that obtained using periodic self-reported questionnaires, indicating that using location data is a promising direction for predicting depression treatment outcome.