Cargando…
A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study
BACKGROUND: The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical data such as clinical notes and diagnostic imaging results. However, such information may not be rea...
Autores principales: | Chen, Min, Tan, Xuan, Padman, Rema |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926350/ https://www.ncbi.nlm.nih.gov/pubmed/36716097 http://dx.doi.org/10.2196/36477 |
Ejemplares similares
-
Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)
por: Uchida, Kazutaka, et al.
Publicado: (2021) -
Using machine learning to improve risk prediction in durable left ventricular assist devices
por: Kilic, Arman, et al.
Publicado: (2021) -
Clinical Prediction Rules to Classify Types of Stroke at Prehospital Stage: Japan Urgent Stroke Triage (JUST) Score
por: Uchida, Kazutaka, et al.
Publicado: (2018) -
End to end stroke triage using cerebrovascular morphology and machine learning
por: Deshpande, Aditi, et al.
Publicado: (2023) -
Developing a common data model approach for DISCOVER CKD: A retrospective, global cohort of real-world patients with chronic kidney disease
por: Kumar, Supriya, et al.
Publicado: (2022)