Paper Info
Title
Discovering COPD phenotyping via simultaneous feature selection and clustering
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a preventable, treatable, and slowly progressive disease, whose course is aggravated by a periodic worsening of symptoms and lung function lasting for several days. The need to implement personalized treatment, where the characteristics of the patients together with disease information will be used to select the best treatment option, has boosted the research for identifying COPD phenotypes. This asks for addressing data clustering and feature selection, but both have shown some weaknesses when applied to this aim. To overcome such limitations, in this work we simultaneously select the discriminative descriptors and cluster the data. Our idea stems from observing that such two tasks are strictly interrelated each other, motivating us for using a method where, iteration by iteration, feature selection influences the clustering step, and viceversa. As a results we discover five phenotypes that, contrary to the traditional classes defined by the Global Initiative for Chronic Obstructive Lung Disease, seem to be prone to specific outcomes. This is of particular importance from a clinical point of view because it will allow a more tailored management of the disease.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621443
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
COPD,phenotypes,clustering,feature selection
COPD,Disease,Feature selection,Obstructive lung disease,Computer science,Slowly progressive disease,Artificial intelligence,Cluster analysis,Discriminative model,Machine learning
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-5386-5489-7
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Mario Merone1145.72
Panaiotis Finamore200.34
Claudio Pedone310.69
Raffaele Antonelli Incalzi421.39
Giulio Iannello541446.75
Paolo Soda640739.44