EMU: Early Mental Health Uncovering Framework and Dataset

Abstract

Mental illnesses are often undiagnosed, demonstrating need for an effective unbiased alternative to traditional screening surveys. For this we propose our Early Mental Health Uncovering (EMU) framework that supports near instantaneous mental illness screening with non-intrusive active and passive modalities. We designed, deployed, and evaluated the EMU app to passively collect retrospective digital phenotype data and actively collect short voice recordings. Additionally, the EMU app also administered depression and anxiety screening surveys to produce depression and anxiety screening labels for the data. Notably, more than twice as many participants elected to share scripted audio recordings than any passive modality. We then study the effectiveness of machine learning models trained with the active modalities. Using scripted audio, EMU screens for depression with F1=0.746, anxiety with F1=0.667, and suicidal ideation with F1=0.706. Using unscripted audio, EMU screens for depression with F1=0.691, anxiety with F1=0.636, and suicidal ideation with F1=0.667. Jitter is an important feature for screening with scripted audio, while Mel-Frequency Cepstral Coefficient is an important feature for screening with unscripted audio. Further, the frequency of help-related words carried a strong signal for suicidal ideation screening with unscripted audio transcripts. This research results in a deeper understanding of the selection of modalities and corresponding features for mobile screening. The EMU dataset will be made available to public domain, representing valuable data resource for the community to further advance universal mental illness screening research.