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Sequence multi-task learning to forecast mental wellbeing from sparse self-reported data

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Servia-Rodriguez, S 
Farrahi, K 
Rentfrow, J 

Abstract

Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers.

Our experiments using a real-world dataset of 33, 000 user-weeks revealed that (i) 3 weeks of sparsely reported mood is the optimal number to accurately forecast mood, (ii) multi-task learning models both dimensions of mood –valence and arousal– with higher accuracy than separate or traditional ML models, and (iii) mood variability, personality traits and day of the week play a key role in the performance of our model. We believe this work provides psychologists and developers of future mobile mental health applications with a ready-to-use and effective tool for early diagnosis of mental health issues at scale.

Description

Keywords

multi-task learning, sequence learning, recurrent neural networks, mood forecasting

Journal Title

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference Name

KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Journal ISSN

Volume Title

Publisher

ACM
Sponsorship
Engineering and Physical Sciences Research Council (EP/I032673/1)
Engineering and Physical Sciences Research Council (EP/N509620/1)
EPSRC (2178667)
This work was supported by the Embiricos Trust Scholarship of Jesus College Cambridge, EPSRC through Grants DTP (EP/N509620/1) and UBHAVE (EP/I032673/1), and Nokia Bell Labs through the Centre of Mobile, Wearable Systems and Augmented Intelligence.