کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
518476 867594 2009 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Explorative data analysis techniques and unsupervised clustering methods to support clinical assessment of Chronic Obstructive Pulmonary Disease (COPD) phenotypes
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Explorative data analysis techniques and unsupervised clustering methods to support clinical assessment of Chronic Obstructive Pulmonary Disease (COPD) phenotypes
چکیده انگلیسی

Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide and represents one of the major causes of chronic morbidity. Cigarette smoking is the most important risk factor for COPD. In these patients, the airflow limitation is caused by a mixture of small airways disease and parenchyma destruction, the relative contribution of which varies from person to person. The twofold nature of the pathology has been studied in the past and according to some authors each patient should be classified as presenting a predominantly bronchial or emphysematous phenotype. In this study we applied various explorative analysis techniques (PCA, MCA, MDS) and recent unsupervised clustering methods (KHM) to study a large dataset, acquired from 415 COPD patients, to assess the presence of hidden structures in data corresponding to the different COPD phenotypes observed in clinical practice. In order to validate our methods, we compared the results obtained from a training set of 415 patients with lung density data acquired in a test set of 93 patients who underwent HRCT (High Resolution Computerized Tomography).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Biomedical Informatics - Volume 42, Issue 6, December 2009, Pages 1013–1021
نویسندگان
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