  Previous studies have shown the correlation between sensor data collected
from mobile phones and human depression states. Compared to the traditional
self-assessment questionnaires, the passive data collected from mobile phones
is easier to access and less time-consuming. In particular, passive mobile
phone data can be collected on a flexible time interval, thus detecting
moment-by-moment psychological changes and helping achieve earlier
interventions. Moreover, while previous studies mainly focused on depression
diagnosis using mobile phone data, depression forecasting has not received
sufficient attention. In this work, we extract four types of passive features
from mobile phone data, including phone call, phone usage, user activity, and
GPS features. We implement a long short-term memory (LSTM) network in a
subject-independent 10-fold cross-validation setup to model both a diagnostic
and a forecasting tasks. Experimental results show that the forecasting task
achieves comparable results with the diagnostic task, which indicates the
possibility of forecasting depression from mobile phone sensor data. Our model
achieves an accuracy of 77.0 % for major depression forecasting (binary), an
accuracy of 53.7 % for depression severity forecasting (5 classes), and a best
RMSE score of 4.094 (PHQ-9, range from 0 to 27).
