A machine learning approach to prediction of exacerbations of chronic obstructive pulmonary disease

Abstract

Chronic Obstructive Pulmonary Disease (COPD) places an enormous burden on the health care systems and causes diminished health related quality of life. The highest proportion of human and economic cost is associated to admissions for acute exacerbation of respiratory symptoms. The remote monitoring of COPD patients with the view of early detection of acute exacerbation of COPD (AECOPD) is one of the goals of the respiratory community. In this study, machine learning was used to develop predictive models. Models robustness to exacerbation definition was analyzed. A non-knowled-ge based approach was followed on data self-reported by patients using a multimodal tool during a remote monitoring 6 months trial. Comparison of different classifier algorithms operating with different AECOPD definitions was performed. Significant results were obtained for AECOPD prediction, regardless of the definition of exacerbation used. Best accuracy was achieved using a PNN classifier independently of the selected AECOPD definition. Our study suggests that the proposed data-driven methodology could help to design reliable predictive algorithms aimed to predict COPD exacerbations and therefore could provide support both to physicians and patients.