A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department

A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department

Abstract Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patte...

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Bibliographic Details
Main Authors: Michael S. A. Niemantsverdriet, Titus A. P. de Hond, Imo E. Hoefer, Wouter W. van Solinge, Domenico Bellomo, Jan Jelrik Oosterheert, Karin A. H. Kaasjager, Saskia Haitjema
Format: Article
Language:English
Published: BMC 2022-12-01
Series:BMC Emergency Medicine
Subjects:
Sepsis
Electronic health records
Emergency department
Machine learning
Endpoint adjudication
Online Access:https://doi.org/10.1186/s12873-022-00764-9

Internet

https://doi.org/10.1186/s12873-022-00764-9

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