Cargando…
Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data
Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a...
Autores principales: | Valik, John Karlsson, Ward, Logan, Tanushi, Hideyuki, Johansson, Anders F., Färnert, Anna, Mogensen, Mads Lause, Pickering, Brian W., Herasevich, Vitaly, Dalianis, Hercules, Henriksson, Aron, Nauclér, Pontus |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359402/ https://www.ncbi.nlm.nih.gov/pubmed/37474597 http://dx.doi.org/10.1038/s41598-023-38858-4 |
Ejemplares similares
-
Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
por: Valik, John Karlsson, et al.
Publicado: (2020) -
The accuracy of fully automated algorithms for surveillance of healthcare-onset Clostridioides difficile infections in hospitalized patients
por: van der Werff, Suzanne Desirée, et al.
Publicado: (2022) -
Clinical phenotypes and outcomes of SARS-CoV-2, influenza, RSV and seven other respiratory viruses: a retrospective study using complete hospital data
por: Hedberg, Pontus, et al.
Publicado: (2022) -
Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
por: Ehrentraut, Claudia, et al.
Publicado: (2016) -
Ensembles of randomized trees using diverse distributed representations of clinical events
por: Henriksson, Aron, et al.
Publicado: (2016)