Cargando…
Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study
OBJECTIVE: To predict older adults’ risk of avoidable hospitalisation related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada. DESIGN, SETTING AND PARTICIPANTS: A retrospective cohort study was conducted on a large cohort...
Autores principales: | Yi, Seung Eun, Harish, Vinyas, Gutierrez, Jahir, Ravaut, Mathieu, Kornas, Kathy, Watson, Tristan, Poutanen, Tomi, Ghassemi, Marzyeh, Volkovs, Maksims, Rosella, Laura C |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977821/ https://www.ncbi.nlm.nih.gov/pubmed/35365510 http://dx.doi.org/10.1136/bmjopen-2021-051403 |
Ejemplares similares
-
Developing Machine Learning Algorithms on Routinely Collected Administrative Health Data - Lessons from Ontario, Canada.
por: Harish, Vinyas, et al.
Publicado: (2022) -
Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes
por: Ravaut, Mathieu, et al.
Publicado: (2021) -
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data
por: Ravaut, Mathieu, et al.
Publicado: (2021) -
Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees
por: Gutierrez, Jahir M., et al.
Publicado: (2021) -
Machine learning and health need better values
por: Ghassemi, Marzyeh, et al.
Publicado: (2022)