title: Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare.
abstract: We investigated whether physiological data can be used for predicting chronic obstructive pulmonary disease (COPD) exacerbations. Home measurements from 57 patients were analysed, during which 10 exacerbations occurred in nine patients. A total of 273 different features were evaluated for their discrimination abilities between periods with and without exacerbations. The analysis showed that if a sensitivity level of 70% is considered to be acceptable, then the specificity was 95% and the AUC was 0.73, i.e. it is possible to discriminate between periods of exacerbation and periods without. A system capable of predicting risk could provide support to COPD patients in their tele-rehabilitation.
authors.affiliation: Department of Health Science and Technology, Aalborg University, Denmark. mhj@hst.aau.dk
authors.forename: Morten H
authors.initials: MH
authors.lastname: Jensen
authors.forename: Simon L
authors.initials: SL
authors.lastname: Cichosz
authors.forename: Birthe
authors.initials: B
authors.lastname: Dinesen
authors.forename: Ole K
authors.initials: OK
authors.lastname: Hejlesen
mesh_terms: D000208:Acute Disease; D000368:Aged; D018450:Disease Progression; D005260:Female; D005544:Forecasting / Q000379:methods*; D006760:Hospitalization / Q000191:economics / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D029424:Pulmonary Disease, Chronic Obstructive / Q000150:complications* / Q000175:diagnosis / Q000628:therapy*; D017216:Telemedicine / Q000379:methods*