Mobile Depression Screening with Time Series of Text Logs and Call Logs

Abstract

Passively detecting depression is important to achieve universal screening. As text and call logs have been overlooked as potential passive screening modalities, we construct time series from these text and call logs using the number of communications, number of contacts, and average length of communications. We calculate these values for every 4, 6, 12, and 24 hours. We leverage a time series feature extraction library to extract features for machine learning models. Our results show that outgoing texts are more predictive of depression than incoming texts but incoming calls are more predictive of depression than outgoing calls. Specifically, we are able to achieve an F1 score of 0.72 with average text length and an F1 score of 0.65 with call duration. This detailed exploration into the ability of text and call logs to screen for depression will help guide future research in this domain.