A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis.

Journal: Medicina intensiva

Published Date: Sep 20, 2018

Abstract

INTRODUCTION: Sepsis is associated to a high mortality rate, and its severity must be evaluated quickly. The severity of illness scores used are intended to be applicable to all patient populations, and generally evaluate in-hospital mortality. However, patients with sepsis continue to be at risk of death after hospital discharge.

Authors

J E García-Gallo

Engineering and Software Investigation Group, Universidad de Antioquia UdeA, Medellín, Colombia. Electronic address: jesteban.garcia@udea.edu.co.
N J Fonseca-Ruiz

Critical and Intensive Care, Medellín Clinic, Medellín, Colombia; Critical and Intensive Care Program, CES University, Medellín, Colombia.
L A Celi

Laboratory of Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, Cambridge, USA.
J F Duitama-Muñoz

Engineering and Software Investigation Group, Universidad de Antioquia UdeA, Medellín, Colombia.

Keywords

Aged Algorithms Area Under Curve Critical Illness Female Forecasting Humans Intensive Care Units Machine Learning Male Middle Aged Models, Statistical Registries Retrospective Studies ROC Curve Sepsis Severity of Illness Index Time Factors

External Resources

View on PubMed Access via DOI PubMed (30245121)

A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis.

Abstract

Authors

Keywords

External Resources

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