Characterization of patients with heart failure with preserved ejection fraction using machine learning

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• dc.contributor.author Martí Castellote, Pablo Miki
• dc.date.accessioned 2019-11-08T14:17:32Z
• dc.date.available 2019-11-08T14:17:32Z
• dc.date.issued 2019-07
• dc.description Treball fi de màster de: Master in Computational Biomedical Engineeringca
• dc.description Tutors: Bart Bijnens, Sergio Sánchez-Martínez i Scott D. Solomon
• dc.description.abstract The pathophysiological complexity and heterogeneity of the heart failure with preserved ejection fraction (HFpEF) syndrome is not fully captured by clinical guidelines, which oversimplify the condition to standardize diagnosis, thus leading to suboptimal diagnosis. Machine learning (ML) tools have proven useful at the time to find therapeutically homogeneous patient subclasses, which can potentially improve the prognosis of HFpEF. The aim of this project is to find archetypal patients and obtain information about different mechanistic processes underlying the worsening of the HFpEF syndrome. This goal is in line with the personalized medicine paradigm, allowing for patient-specific treatments instead of the one-size-fits-all approach used in the current clinical guidelines. More precisely, this paper presents the analysis of a cohort of patients with HFpEF from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) clinical trial. Clinical and demographic descriptors are used as well as complex echocardiographic descriptors of cardiac function such as the trans-mitral inflow Doppler trace, the aortic outflow Doppler trace and full cycle traces of regional left ventricular strain assessed by 2D speckle tracking on the apical 4-chamber view. The analysis is based on the reduction of data dimensionality through manifold learning and subsequent regression and clustering. Firstly, the use of unsupervised Multiple Kernel Learning for dimensionality reduction (MKL-DR) makes it possible to combine the information from the different descriptors to generate a space of reduced dimensions. Regression to the input descriptors as a function of the newly computed dimensions makes it easy to interpret how the variance is embedded in the new representation. Lastly, clustering in the resulting space resulted in three homogenous patient groups, which help resolve heterogeneity in the overall population.ca
• dc.format.mimetype application/pdf*
• dc.identifier.uri http://hdl.handle.net/10230/42815
• dc.language eng
• dc.language.iso engca
• dc.rights Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanyaca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/esca
• dc.subject.keyword Heart Failure with preserved Ejection Fraction
• dc.subject.keyword Ejection fraction
• dc.subject.keyword Machine Learning
• dc.subject.keyword Unsupervised Multiple Kernel Learning
• dc.subject.keyword Kernel Regression
• dc.subject.keyword Clustering
• dc.subject.other Insuficiència cardíaca
• dc.subject.other Insuficiència cardíaca
• dc.subject.other Kernel, Funcions de
• dc.title Characterization of patients with heart failure with preserved ejection fraction using machine learningca
• dc.type info:eu-repo/semantics/masterThesisca