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Data processing pipeline for cardiogenic shock prediction using machine learning
N. Jajcay, B. Bezak, A. Segev, S. Matetzky, J. Jankova, M. Spartalis, M. El Tahlawi, F. Guerra, J. Friebel, T. Thevathasan, I. Berta, L. Pölzl, F. Nägele, E. Pogran, FA. Cader, M. Jarakovic, C. Gollmann-Tepeköylü, M. Kollarova, K. Petrikova, O....
Status not-indexed Language English Country Switzerland
Document type Journal Article
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- Journal Article MeSH
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.
3rd Department of Cardiology National and Kapodistrian University of Athens Athens Greece
3rd Medical Department Cardiology and Intensive Care Medicine Wilhelminen Hospital Vienna Austria
Affiliated to the Sackler Faculty of Medicine Tel Aviv University Tel Aviv Israel
Anesthesia and Intensive Care Fondazione Policlinico San Matteo Hospital IRCCS Pavia Italy
Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany
Cardiology Department Emergency County Clinical Hospital of Oradea Oradea Romania
Clinic of Cardiac Surgery National Institute of Cardiovascular Diseases Bratislava Slovakia
Department of Acute Cardiology National Institute of Cardiovascular Diseases Bratislava Slovakia
Department of Cardiology Faculty of Human Medicine Zagazig University Zagazig Egypt
Department of Cardiology Ibrahim Cardiac Hospital and Research Institute Dhaka Bangladesh
Department of Clinical Surgical Diagnostic and Paediatric Sciences University of Pavia Pavia Italy
Department of Internal Medicine 2 Division of Cardiology Medical University of Vienna Vienna Austria
Deutsches Zentrum für Herz Kreislauf Forschung e 5 Berlin Germany
Duke Clinical Research Institute Durham NC United States
Faculty of Medicine Comenius University in Bratislava Bratislava Slovakia
Faculty of Medicine University of Novi Sad Novi Sad Serbia
Global Clinical Scholars Research Training Program Harvard Medical School Boston MA United States
Institute of Medical Informatics Charité Universitätsmedizin Berlin Berlin Germany
Premedix Academy Bratislava Slovakia
The Leviev Cardiothoracic and Vascular Center Chaim Sheba Medical Center Ramat Gan Israel
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