Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Unexpected ICU readmission is associated with longer length of stay and increased mortality. To prevent ICU readmission and death after ICU discharge, our team of intensivists and data scientists aimed to use AmsterdamUMCdb to develop an explainable machine learning–based real-time bedside decision...

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Detalles Bibliográficos
Autores principales: Thoral, Patrick J., Fornasa, Mattia, de Bruin, Daan P., Tonutti, Michele, Hovenkamp, Hidde, Driessen, Ronald H., Girbes, Armand R. J., Hoogendoorn, Mark, Elbers, Paul W. G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
Predictive Modeling Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8437217/
https://www.ncbi.nlm.nih.gov/pubmed/34589713
http://dx.doi.org/10.1097/CCE.0000000000000529

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8437217/
https://www.ncbi.nlm.nih.gov/pubmed/34589713
http://dx.doi.org/10.1097/CCE.0000000000000529

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