Probabilistic models for smart monitoring

Applying artificial intelligence techniques to management of chronic diseases - smart monitoring - has great potential to improve chronic disease care. Probabilistic models offer powerful methods for automatic data inter-pretation, and thus play a potentially large role in mobile, personalised care. In particular in the context of disease monitoring one needs clinical time-series data that include data of multiple patient parameters, to allow building such models. However, in practice clinical time-series data of patients with chronic disease are only limited available, and when they are available usually only of a few patients. In this paper, we explore different ways to build predictive models for the detection of COPD exacerbations and related hospitalisation, focusing on the temporal aspect of monitoring data while taking into account data sparsity. Preliminary results indicate that even with the limited data available some predictions can be made about hospitalisation.