The University of Southampton
University of Southampton Institutional Repository

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

Sequence multi-task learning to forecast mental wellbeing from sparse self-reported data
Sequence multi-task learning to forecast mental wellbeing from sparse self-reported data
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.
2886-2894
ACM
Spathis, Dimitris
0352ccc2-a97c-4273-998a-0bc8546612fa
Servia-Rodriguez, Sandra
cb942d57-9954-4e9e-9bcf-bf2309d774b0
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Mascolo, Cecilia
78286d55-4fd6-41c3-bd6b-4a41baf46694
Rentfrow, Jason
a80783c0-af6f-4164-88ca-9a7e71c3a90e
Teredesai, Ankur
Kumar, Vipin
Spathis, Dimitris
0352ccc2-a97c-4273-998a-0bc8546612fa
Servia-Rodriguez, Sandra
cb942d57-9954-4e9e-9bcf-bf2309d774b0
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Mascolo, Cecilia
78286d55-4fd6-41c3-bd6b-4a41baf46694
Rentfrow, Jason
a80783c0-af6f-4164-88ca-9a7e71c3a90e
Teredesai, Ankur
Kumar, Vipin

Spathis, Dimitris, Servia-Rodriguez, Sandra, Farrahi, Katayoun, Mascolo, Cecilia and Rentfrow, Jason (2019) Sequence multi-task learning to forecast mental wellbeing from sparse self-reported data. Teredesai, Ankur and Kumar, Vipin (eds.) In KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM. pp. 2886-2894 . (doi:10.1145/3292500.3330730).

Record type: Conference or Workshop Item (Paper)

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.

Text
KDD '19 camera ready June11 - Accepted Manuscript
Download (5MB)

More information

Accepted/In Press date: 1 June 2019
e-pub ahead of print date: 25 July 2019
Published date: August 2019

Identifiers

Local EPrints ID: 431885
URI: http://eprints.soton.ac.uk/id/eprint/431885
PURE UUID: 5ad98e60-5f76-43c5-8c6d-1925f6c467d3

Catalogue record

Date deposited: 20 Jun 2019 16:30
Last modified: 06 Oct 2020 21:13

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×