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Bayesian Transfer Learning for the Prediction of Self-reported Well-being Scores

Christinaki, Eirini and Poli, Riccardo and Citi, Luca (2018) Bayesian Transfer Learning for the Prediction of Self-reported Well-being Scores. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018-07-18 - 2018-07-21, Honolulu, HI, USA.

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Abstract

Predicting the severity and onset of depressive symptoms is of great importance. User-specific models have better performance than a general model but require significant amounts of training data from each individual, which is often impractical to obtain. Even when this is possible, there is a significant lag between the beginning of the data-collection phase and when the system is completely trained and thus able to start making useful predictions. In this study, we propose a transfer learning Bayesian modelling method based on a Markov Chain Monte Carlo (MCMC) sampler and Bayesian model averaging for dealing with the challenge of building user-specific predictive models able to make predictions of self-reported well-being scores with limited sparse training data. The evaluation of our method using real-world data collected within the NEVERMIND project showed a better predictive performance for the transfer learning model compared to conventional learning with no transfer.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 01 Feb 2019 14:52
Last Modified: 01 Feb 2019 14:52
URI: http://repository.essex.ac.uk/id/eprint/23932

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