Data processing pipeline for cardiogenic shock prediction using machine learning

Data processing pipeline for cardiogenic shock prediction using machine learning

IntroductionRecent 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...

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Bibliographic Details
Main Authors: 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 Tica, Konstantin A. Krychtiuk, Guido Tavazzi, Carsten Skurk, Kurt Huber, Allan Böhm
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-03-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
classification
machine learning
missing data imputation
processing pipeline
prediction model
cardiogenic shock
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2023.1132680/full

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https://www.frontiersin.org/articles/10.3389/fcvm.2023.1132680/full

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