Cargando…
Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial
INTRODUCTION: Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a...
Autores principales: | Shimabukuro, David W, Barton, Christopher W, Feldman, Mitchell D, Mataraso, Samson J, Das, Ritankar |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5687546/ https://www.ncbi.nlm.nih.gov/pubmed/29435343 http://dx.doi.org/10.1136/bmjresp-2017-000234 |
Ejemplares similares
-
Lactate Clearance Predicts Survival Among Patients in the Emergency Department with Severe Sepsis
por: Bhat, Sundeep R., et al.
Publicado: (2015) -
Predicting pulmonary embolism among hospitalized patients with machine learning algorithms
por: Ryan, Logan, et al.
Publicado: (2022) -
Wound Botulism in Injection Drug Users: Time to Antitoxin Correlates with Intensive Care Unit Length of Stay
por: Offerman, Steven R., et al.
Publicado: (2009) -
Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units
por: McCoy, Andrea, et al.
Publicado: (2017) -
Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm
por: Maharjan, Jenish, et al.
Publicado: (2022)