Data processing pipeline for cardiogenic shock prediction using machine learning

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 car...

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Detalles Bibliográficos
Autores principales: Jajcay, Nikola, Bezak, Branislav, Segev, Amitai, Matetzky, Shlomi, Jankova, Jana, Spartalis, Michael, El Tahlawi, Mohammad, Guerra, Federico, Friebel, Julian, Thevathasan, Tharusan, Berta, Imrich, Pölzl, Leo, Nägele, Felix, Pogran, Edita, Cader, F. Aaysha, Jarakovic, Milana, Gollmann-Tepeköylü, Can, Kollarova, Marta, Petrikova, Katarina, Tica, Otilia, Krychtiuk, Konstantin A., Tavazzi, Guido, Skurk, Carsten, Huber, Kurt, Böhm, Allan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Cardiovascular Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077147/
https://www.ncbi.nlm.nih.gov/pubmed/37034352
http://dx.doi.org/10.3389/fcvm.2023.1132680

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077147/
https://www.ncbi.nlm.nih.gov/pubmed/37034352
http://dx.doi.org/10.3389/fcvm.2023.1132680

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