Automatic generation of personalised alert thresholds for patients with COPD

Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease predicted to become the third leading cause of death by 2030. Patients with COPD are at risk of exacerbations in their symptoms, which have an adverse effect on their quality of life and may require emergency hospital admission. Using the results of a pilot study of an m-Health system for COPD self-management and tele-monitoring, we demonstrate a data-driven approach for computing personalised alert thresholds to prioritise patients for clinical review. Univariate and multivariate methodologies are used to analyse and fuse daily symptom scores, heart rate, and oxygen saturation measurements. We discuss the benefits of a multivariate kernel density estimator which improves on univariate approaches.