DocumentCode :
4690
Title :
A Multiscale Approach for Modeling Atherosclerosis Progression
Author :
Exarchos, Konstantinos P. ; Carpegianni, Clara ; Rigas, Georgios ; Exarchos, Themis P. ; Vozzi, Federico ; Sakellarios, Antonis ; Marraccini, Paolo ; Naka, Katerina ; Michalis, Lambros ; Parodi, Oberdan ; Fotiadis, Dimitrios I.
Author_Institution :
Dept. of Mater. Sci. & Eng., Univ. of Ioannina, Ioannina, Greece
Volume :
19
Issue :
2
fYear :
2015
fDate :
Mar-15
Firstpage :
709
Lastpage :
719
Abstract :
Progression of atherosclerotic process constitutes a serious and quite common condition due to accumulation of fatty materials in the arterial wall, consequently posing serious cardiovascular complications. In this paper, we assemble and analyze a multitude of heterogeneous data in order to model the progression of atherosclerosis (ATS) in coronary vessels. The patient´s medical record, biochemical analytes, monocyte information, adhesion molecules, and therapy-related data comprise the input for the subsequent analysis. As indicator of coronary lesion progression, two consecutive coronary computed tomography angiographies have been evaluated in the same patient. To this end, a set of 39 patients is studied using a twofold approach, namely, baseline analysis and temporal analysis. The former approach employs baseline information in order to predict the future state of the patient (in terms of progression of ATS). The latter is based on an approach encompassing dynamic Bayesian networks whereby snapshots of the patient´s status over the follow-up are analyzed in order to model the evolvement of ATS, taking into account the temporal dimension of the disease. The quantitative assessment of our work has resulted in 93.3% accuracy for the case of baseline analysis, and 83% overall accuracy for the temporal analysis, in terms of modeling and predicting the evolvement of ATS. It should be noted that the application of the SMOTE algorithm for handling class imbalance and the subsequent evaluation procedure might have introduced an overestimation of the performance metrics, due to the employment of synthesized instances. The most prominent features found to play a substantial role in the progression of the disease are: diabetes, cholesterol and cholesterol/HDL. Among novel markers, the CD11b marker of leukocyte integrin complex is associated with coronary plaque progression.
Keywords :
Bayes methods; adhesion; angiocardiography; biochemistry; blood vessels; cardiovascular system; cellular biophysics; computerised tomography; diagnostic radiography; diseases; medical image processing; SMOTE algorithm; adhesion molecules; arterial wall; atherosclerosis progression modeling; baseline analysis; baseline information; biochemical analytes; cardiovascular complications; cholesterol-HDL; consecutive coronary computed tomography angiographies; coronary lesion progression; coronary plaque progression; coronary vessels; diabetes; disease; dynamic Bayesian networks; fatty materials; heterogeneous data; leukocyte integrin complex; monocyte information; multiscale approach; patient medical record; patient status; performance metrics overestimation; subsequent analysis; temporal analysis; temporal dimension; therapy-related data; Artificial neural networks; Classification algorithms; Diabetes; Diseases; Niobium; Radio frequency; Support vector machines; Atherosclerosis (ATS) progression; classification; dynamic Bayesian networks;
fLanguage :
English
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
Publisher :
ieee
ISSN :
2168-2194
Type :
jour
DOI :
10.1109/JBHI.2014.2323935
Filename :
6815641
Link To Document :
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