Cargando…
EXPLAINABLE MACHINE-LEARNING FOR PREDICTING PREOPERATIVE FRAILTY PHENOTYPE USING ELECTRONIC HEALTH RECORDS
Pre-operative frailty among patients is strongly associated with poor post-operative outcomes. Operationalizing frailty in clinical practice is challenging due to the lack of resources and pragmatic complexities. Feasible tools are needed to cover the scarcity in this area. Harnessing electronic hea...
Autores principales: | Mardini, Mamoun, Price, Catherine, Tighe, Patrick, Manini, Todd |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9766754/ http://dx.doi.org/10.1093/geroni/igac059.2127 |
Ejemplares similares
-
IDENTIFYING THE FRAILTY COMPONENTS THAT ARE MOST IMPORTANT IN PREDICTING POSTSURGICAL OUTCOMES
por: Mardini, Mamoun, et al.
Publicado: (2022) -
POINT-OF-CARE TESTING FOR THE FRAILTY PHENOTYPE PREDICTS POSTSURGICAL OUTCOMES
por: Mardini, Mamoun, et al.
Publicado: (2022) -
Are Machine Learning Models Used to Represent Accelerometry Data Robust to Age Differences?
por: Manini, Todd, et al.
Publicado: (2021) -
Smart Wearables in the Lens of Aging: Results From the ROAMM Study
por: Mardini, Mamoun, et al.
Publicado: (2020) -
Older Adults' Satisfaction and Compliance of Smartwatches Providing Ecological Momentary
por: Laborde, Charlotte, et al.
Publicado: (2020)