Cargando…
Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland
AIM: To show whether adding blood glucose to the National Early Warning Score (NEWS) parameters in a machine learning model predicts 30-day mortality more precisely than the standard NEWS in a prehospital setting. METHODS: In this study, vital sign data prospectively collected from 3632 unselected p...
Autores principales: | Tamminen, Joonas, Kallonen, Antti, Hoppu, Sanna, Kalliomäki, Jari |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244527/ https://www.ncbi.nlm.nih.gov/pubmed/34223354 http://dx.doi.org/10.1016/j.resplu.2021.100089 |
Ejemplares similares
-
Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study
por: Pirneskoski, Jussi, et al.
Publicado: (2020) -
Ability of the National Early Warning Score and its respiratory and haemodynamic subcomponents to predict short-term mortality on general wards: a prospective three-centre observational study in Finland
por: Loisa, Eetu, et al.
Publicado: (2022) -
Trends in the national early warning score are associated with subsequent mortality – A prospective three-centre observational study with 11,331 general ward patients
por: Loisa, Eetu, et al.
Publicado: (2022) -
Prehospital administration of blood products: experiences from a Finnish physician-staffed helicopter emergency medical service
por: Vuorinen, Pauli, et al.
Publicado: (2020) -
Spontaneous trigger words associated with confirmed out-of-hospital cardiac arrest: a descriptive pilot study of emergency calls
por: Tamminen, Joonas, et al.
Publicado: (2020)