Data processing pipeline for cardiogenic shock prediction using machine learning.
Front Cardiovasc Med
; 10: 1132680, 2023.
Article
em En
| MEDLINE
| ID: mdl-37034352
ABSTRACT
Introduction:
Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS.Methods:
We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)-based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction.Results:
We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization.Conclusion:
We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.
Texto completo:
1
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Front Cardiovasc Med
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Eslováquia