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