Well-being Forecasting using a Parametric Transfer-Learning method based on the Fisher Divergence and Hamiltonian Monte Carlo

Eirini Christinaki; Tasos Papastylianou; Sara Carletto; Sergio Gonzalez-Martinez; Luca Ostacoli; Manuel Ottaviano; Riccardo Poli; Luca Citi

Research Article

Well-being Forecasting using a Parametric Transfer-Learning method based on the Fisher Divergence and Hamiltonian Monte Carlo

Download949 downloads

Cite: BibTeX Plain Text

@ARTICLE{10.4108/eai.16-10-2020.166661,
    author={Eirini Christinaki and Tasos Papastylianou and Sara Carletto and Sergio Gonzalez-Martinez and Luca Ostacoli and Manuel Ottaviano and Riccardo Poli and Luca Citi},
    title={Well-being Forecasting using a Parametric Transfer-Learning method based on the Fisher Divergence and Hamiltonian Monte Carlo},
    journal={EAI Endorsed Transactions on Bioengineering and Bioinformatics},
    volume={1},
    number={1},
    publisher={EAI},
    journal_a={BEBI},
    year={2020},
    month={10},
    keywords={Transfer Learning, MCMC, Bayesian Inference, Well-being Prediction, Personalised Modelling, NEVERMIND},
    doi={10.4108/eai.16-10-2020.166661}
}

Eirini Christinaki
Tasos Papastylianou
Sara Carletto
Sergio Gonzalez-Martinez
Luca Ostacoli
Manuel Ottaviano
Riccardo Poli
Luca Citi
Year: 2020
Well-being Forecasting using a Parametric Transfer-Learning method based on the Fisher Divergence and Hamiltonian Monte Carlo
BEBI
EAI
DOI: 10.4108/eai.16-10-2020.166661

Eirini Christinaki¹, Tasos Papastylianou¹, Sara Carletto², Sergio Gonzalez-Martinez³, Luca Ostacoli⁴, Manuel Ottaviano³, Riccardo Poli¹, Luca Citi¹^,*

1: School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
2: Department of Neuroscience “Rita Levi Montalcini”, Università degli Studi di Torino, Turin, Italy
3: Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain
4: Department of Clinical and Biological Sciences, Università degli Studi di Torino, Turin, Italy

*Contact email: lciti@essex.ac.uk

Abstract

INTRODUCTION: Traditional personalised modelling typically requires sufficient personal data for training. This is a challenge in healthcare contexts, e.g. when using smartphones to predict well-being.

OBJECTIVE: A method to produce incremental patient-specific models and forecasts even in the early stages of data collection when the data are sporadic and limited.

METHODS: We propose a parametric transfer-learning method based on the Fisher divergence, where information from other patients is injected as a prior term into a Hamiltonian Monte Carlo framework. We test our method on the NEVERMIND dataset of self-reported well-being scores.

RESULTS: Out of 54 scenarios representing varying training/forecasting lengths and competing methods, our method achieved overall best performance in 50 (92.6%) and demonstrated a significant median difference in45 (83.3%).

CONCLUSION: The method performs favourably overall, particularly when long-term forecasts are required given short-term data.

Keywords: Transfer Learning, MCMC, Bayesian Inference, Well-being Prediction, Personalised Modelling, NEVERMIND

Received: 2020-06-09
Accepted: 2020-10-01
Published: 2020-10-16
Publisher: EAI

: http://dx.doi.org/10.4108/eai.16-10-2020.166661

Copyright © 2020 E. Christinaki, T. Papastylianou et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.