Data processing pipeline for cardiogenic shock prediction using machine learning

Nikola Jajcay, Branislav Bezak*, Amitai Segev, Shlomi Matetzky, Jana Jankova, Michael Spartalis, Mohammad El Tahlawi, Federico Guerra, Julian Friebel, Tharusan Thevathasan, Imrich Berta, Leo Pölzl, Felix Nägele, Edita Pogran, F. Aaysha Cader, Milana Jarakovic, Can Gollmann-Tepeköylü, Marta Kollarova, Katarina Petrikova, Otilia TicaKonstantin A. Krychtiuk, Guido Tavazzi, Carsten Skurk, Kurt Huber, Allan Böhm

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number1132680
JournalFrontiers in Cardiovascular Medicine
StatePublished - 2023


  • cardiogenic shock
  • classification
  • machine learning
  • missing data imputation
  • prediction model
  • processing pipeline


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