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

Objectives:. 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 beds...

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Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Patrick J. Thoral, MD, Mattia Fornasa, PhD, Daan P. de Bruin, MSc, Michele Tonutti, MRes, Hidde Hovenkamp, MSc, Ronald H. Driessen, Armand R. J. Girbes, MD, PhD, Mark Hoogendoorn, PhD, Paul W. G. Elbers, MD, PhD
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Wolters Kluwer 2021-09-01
Rangatū:	Critical Care Explorations
Urunga tuihono:	http://journals.lww.com/10.1097/CCE.0000000000000529
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

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

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