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

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Wolters Kluwer 2021-09-01
Series:Critical Care Explorations
Online Access:http://journals.lww.com/10.1097/CCE.0000000000000529

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http://journals.lww.com/10.1097/CCE.0000000000000529

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