An artificial intelligence approach to early predict symptom-based exacerbations of COPD

Abstract

Acute exacerbations are one of the main causes that reduce health-related quality of life and lead to hospitalisations of patients of chronic obstructive pulmonary disease (COPD). Prediction of exacerbations could diminish those negative effects and reduce the high costs associated with COPD patients. In this study, 16 patients were telemonitored at home during six months. Respiratory sounds were recorded daily with an electronic sensor ad-hoc designed. In order to enable an automatic prediction of symptom-based exacerbations, recorded data were used to train and validate a decision tree forest classifier. The developed model was capable of predicting early acute exacerbations of COPD, as average, with a 4.4 days margin prior to onset. Thirty-two out of 41 exacerbations were detected early. A percentage of 75.8% (25 out of 33) of detected episodes were reported exacerbation and 87.5% (7 out of 8) were unreported events. The achieved results demonstrated that machine-learning techniques have significant potential to support the early detection of COPD exacerbations.

Keywords

• COPD
• acute exacerbation
• telehealth
• respiratory sounds
• early detection
• prediction
• telemonitoring