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

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 in 45 (83.3%). CONCLUSION: The method performs favourably overall, particularly when long-term forecasts are required given short-term data.

The NEVERMIND e-health system in the treatment of depressive symptoms among patients with severe somatic conditions: A multicentre, pragmatic randomised controlled trial

“…These wearables are based on the PSYCHE system, for which mood classification accuracies of approximately 97% were found. 15 NEVERMIND includes a predictive algorithm (see Christinaki and colleagues 16 for details) that was developed to forecast patients’ depressive symptoms based on the biomedical data collected through the shirt (electrocardiogram, respiration dynamics, body movement), and additional information about mental health symptoms, such as depression, anxiety, sleep problems and stress, collected within the app through psychometric questionnaires and specifically developed questions. The above forecasts were used to optimise the frequency with which the app would present questions to the participants, to allow for an optimal amount of information to be collected while at the same time guarding against ‘questionnaire fatigue’.…”