Cross-platform Prediction of Depression Treatment Outcome Using Location Sensory Data on Smartphones

ABSTRACT 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, subjective, 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. We further explore integrating contrastive learning with domain adaptation to augment data and learn feature embeddings. These learned embeddings are then used to train machine learning models to predict depression treatment outcomes. Our evaluation shows that using the embeddings learned by jointly integrating contrastive learning and domain adaptation leads to the best prediction accuracy. In addition, our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.76. This accuracy is comparable to that obtained using periodic self-reported questionnaires, indicating that using location data is a promising direction for predicting depression treatment outcome. Last, when all location and questionnaire data are used together, the F1 score further increases to 0.79.