NDLI: Prediction of chronic obstructive pulmonary disease exacerbation using physiological time series patterns

Content Provider	IEEE Xplore Digital Library
Author	Yang Xie Redmond, S.J. Mohktar, M.S. Shany, T. Basilakis, J. Hession, M. Lovell, N.H.
Copyright Year	2013
Description	Author affiliation: Dept. of Biomed. Eng., Univ. of Malaya, Kuala Lumpur, Malaysia (Mohktar, M.S.) \|\| Grad. Sch. of Biomed. Eng., Univ. of New South Wales, Sydney, NSW, Australia (Yang Xie; Redmond, S.J.; Shany, T.; Lovell, N.H.) \|\| Sch. of Comput. & Math., Univ. of Western Sydney, Sydney, NSW, Australia (Basilakis, J.) \|\| Blacktown & Mt Druitt Hosp., Sydney, NSW, Australia (Hession, M.)
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.
Starting Page	6784
Ending Page	6787
File Size	189674
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611114
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Market research Physiology Biomedical monitoring Feature extraction Diseases Standards Temperature measurement
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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