Machine-learning based diagnosis of heart failure with preserved ejection fraction: how much do we agree with the guidelines?
| Autor: | Sanchez-Martinez, Sergio, Duchateau, Nicolas, Erdei, Tamas, Kunszt, Gabor, Degiovanni, Anna, Carluccio, Erberto, Fraser, Alan, Piella, Gemma, Bijnens, Bart |
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| Přispěvatelé: | Universitat Pompeu Fabra [Barcelona] (UPF), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Wales Heart Research Institute [Cardiff] (WHRI), Cardiff University, Oslo University Hospital, Department of cardiology, University of Eastern Piedmont, Department of cardiology, Novara, Università degli Studi di Perugia (UNIPG), Università degli Studi di Perugia = University of Perugia (UNIPG) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | EuroEcho-Imaging EuroEcho-Imaging, Dec 2016, Leipzig, Germany |
| Popis: | In press; International audience; Purpose: Current diagnosis of heart failure with preserved ejection fraction (HFPEF) is suboptimal since it oversimplifies abnormalities by only considering "simple" key markers of disease. We investigate whether a comprehensive analysis of multiple myocardial velocity profiles, acquired during a stress echocardiography protocol, can identify characteristic patterns of cardiac (dys-)function and aid in better staging of HFPEF patients.Methods: Velocity profiles were extracted at the basal septum and lateral wall of the left ventricle from rest and exercise tissue Doppler acquisitions. The population consisted of 33 healthy subjects (67±4 years) and 72 HFPEF (72±6 years, diagnosed with the current guidelines). Each cardiac phase was identified and used to temporally align the velocity profiles. An unsupervised machine learning algorithm (multiple kernel learning) was used to fuse the heterogeneous input data and to reduce their complexity. Agglomerative hierarchical clustering was performed on this set to identify different groupings within the population and position each subject with regard to all the others according to their similarity.Results: The identified groups substantially differed for the parameters classically used for diagnosis (E/e’ ratio and 6MWT: p |
| Databáze: | OpenAIRE |
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