Using novel machine learning tools to predict optimal discharge following transcatheter aortic valve replacement.
Journal:
Archives of cardiovascular diseases
PMID:
39424448
Abstract
BACKGROUND: Although transcatheter aortic valve replacement has emerged as an alternative to surgical aortic valve replacement, it requires extensive healthcare resources, and optimal length of hospital stay has become increasingly important. This study was conducted to assess the potential of novel machine learning models (artificial neural network and eXtreme Gradient Boost) in predicting optimal hospital discharge following transcatheter aortic valve replacement.
Authors
Keywords
Aged
Aged, 80 and over
Aortic Valve
Aortic Valve Stenosis
Clinical Decision-Making
Databases, Factual
Decision Support Techniques
Female
Humans
Length of Stay
Machine Learning
Male
Neural Networks, Computer
Patient Discharge
Predictive Value of Tests
Retrospective Studies
Risk Assessment
Risk Factors
Time Factors
Transcatheter Aortic Valve Replacement
Treatment Outcome
United States