Title :
Prediction of chronic obstructive pulmonary disease exacerbation using physiological time series patterns
Author :
Yang Xie ; Redmond, Stephen J. ; Mohktar, Mas S. ; Shany, Tal ; Basilakis, Jim ; Hession, Michael ; Lovell, Nigel H.
Author_Institution :
Grad. Sch. of Biomed. Eng., Univ. of New South Wales, Sydney, NSW, Australia
Abstract :
Chronic obstructive pulmonary disease (COPD) is responsible for significant morbidity and mortality worldwide. Recent clinical research has indicated a strong association between physiological homeostasis and the onset of COPD exacerbation. Thus the analysis of these variables may yield a means of predicting a COPD exacerbation in the near future. However, the accuracy of existing prediction methods based on statistical analysis of periodic snapshots of physiological variables is still far from satisfactory, due to lack of integration of long-term and interactive effects of the physiological variables. Therefore, developing a relatively accurate method for predicting COPD exacerbation is an outstanding challenge. In this paper, a regression-based machine learning technique was developed, using trend pattern variables extracted from COPD patients´ longitudinal physiological records, to classify subjects into “low-risk” and “high-risk” categories, indicating their risk of suffering a COPD exacerbation event. Experimental results from cross validation assessment of the classifier model show an average accuracy of 79.27% using this method.
Keywords :
diseases; learning (artificial intelligence); medical computing; pattern classification; regression analysis; time series; COPD exacerbation; COPD patient longitudinal physiological records; chronic obstructive pulmonary disease exacerbation; classifier model; cross validation assessment; high-risk category; low-risk category; physiological homeostasis; physiological time series patterns; physiological variables; regression-based machine learning technique; statistical analysis; trend pattern variables; Biomedical monitoring; Diseases; Feature extraction; Market research; Physiology; Standards; Temperature measurement; Aged; Aged, 80 and over; Artificial Intelligence; Female; Homeostasis; Humans; Male; Middle Aged; Monitoring, Physiologic; Pulmonary Disease, Chronic Obstructive;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6611114