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

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 patterns in El...

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Detalles Bibliográficos
Autores principales: Niemantsverdriet, Michael S. A., de Hond, Titus A. P., Hoefer, Imo E., van Solinge, Wouter W., Bellomo, Domenico, Oosterheert, Jan Jelrik, Kaasjager, Karin A. H., Haitjema, Saskia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784058/
https://www.ncbi.nlm.nih.gov/pubmed/36550392
http://dx.doi.org/10.1186/s12873-022-00764-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784058/
https://www.ncbi.nlm.nih.gov/pubmed/36550392
http://dx.doi.org/10.1186/s12873-022-00764-9

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