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

Christinaki, Eirini and Papastylianou, Tasos and Carletto, Sara and Gonzalez-Martinez, Sergio and Ostacoli, Luca and Ottaviano, Manuel and Poli, Riccardo and Citi, Luca (2020) 'Well-being Forecasting using a Parametric Transfer-Learning method based on the Fisher Divergence and Hamiltonian Monte Carlo.' EAI Endorsed Transactions on Bioengineering and Bioinformatics. ISSN 2709-4111

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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.

Item Type: Article
Uncontrolled Keywords: Transfer Learning, MCMC, Bayesian Inference, Well-being Prediction, Personalised Modelling, NEVERMIND
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 13 Nov 2020 14:36
Last Modified: 13 Nov 2020 14:36
URI: http://repository.essex.ac.uk/id/eprint/29091

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