ArticleFrontiers in cardiovascular medicine2023
Data processing pipeline for cardiogenic shock prediction using machine learning.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Review
- An artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.Scientific reports · 2025Article
- Machine learning-based scoring system to predict cardiogenic shock in acute coronary syndrome.European heart journal. Digital health · 2025Article
- Profiling of Cardiogenic Shock: Incorporating Machine Learning Into Bedside Management.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.European heart journal. Acute cardiovascular care · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Novel Medical Treatments and Devices for the Management of Heart Failure with Reduced Ejection Fraction.Journal of cardiovascular development and disease · 2024Review
- Review
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Authors and funding
25 authors.
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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.
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