Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement

2020 ◽  
Author(s):  
Arman Kilic ◽  
Anshul Goyal ◽  
James K. Miller ◽  
Thomas G. Gleason ◽  
Artur Dubrawksi
Keyword(s):  
Machine Learning ◽  
Aortic Valve ◽  
Aortic Valve Replacement ◽  
Valve Replacement ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis

2021 ◽  
Vol 11 (11) ◽  
pp. 1062
Author(s):  
Ena Hasimbegovic ◽  
Laszlo Papp ◽  
Marko Grahovac ◽  
Denis Krajnc ◽  
Thomas Poschner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Aortic Valve ◽  
Aortic Stenosis ◽  
Aortic Valve Replacement ◽  
Valve Replacement ◽  
Clinical Decision Making ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction

Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.

scholarly journals Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement

2019 ◽  
Vol 12 (14) ◽  
pp. 1328-1338 ◽  
Author(s):  
Dagmar F. Hernandez-Suarez ◽  
Yeunjung Kim ◽  
Pedro Villablanca ◽  
Tanush Gupta ◽  
Jose Wiley ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Valve ◽  
Aortic Valve Replacement ◽  
Hospital Mortality ◽  
Valve Replacement ◽  
Transcatheter Aortic Valve Replacement ◽  
Prediction Models ◽  
Transcatheter Aortic Valve
Sign in / Sign up

Export Citation Format

Share Document