Cargando…
Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach
BACKGROUND: In clinical practice, therapists often rely on clinical outcome measures to quantify a patient’s impairment and function. Predicting a patient’s discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the p...
Autores principales: | Harari, Yaar, O’Brien, Megan K., Lieber, Richard L., Jayaraman, Arun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288489/ https://www.ncbi.nlm.nih.gov/pubmed/32522242 http://dx.doi.org/10.1186/s12984-020-00704-3 |
Ejemplares similares
-
Wearable airbag technology and machine learned models to mitigate falls after stroke
por: Botonis, Olivia K., et al.
Publicado: (2022) -
Improvement of predictive accuracies of functional outcomes after subacute stroke inpatient rehabilitation by machine learning models
por: Miyazaki, Yuta, et al.
Publicado: (2023) -
The Use of Machine Learning Techniques to Predict Deep Vein
Thrombosis in Rehabilitation Inpatients
por: Hou, Tingting, et al.
Publicado: (2023) -
Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation
por: Say, Irene, et al.
Publicado: (2022) -
A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls
por: Harari, Yaar, et al.
Publicado: (2021)