Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
Abstract Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patient...
Guardado en:
Autores principales: | Juan Lu, Ling Wang, Mohammed Bennamoun, Isaac Ward, Senjian An, Ferdous Sohel, Benjamin J. W. Chow, Girish Dwivedi, Frank M. Sanfilippo |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/4932909e049644539ef7902709d72dbc |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.
por: Jonathon Stewart, et al.
Publicado: (2021) -
Determinants of prehospital coronary heart disease death
por: Ute Amann, et al.
Publicado: (2021) -
Resistance training associated with the administration of anabolic-androgenic steroids improves insulin sensitivity in ovariectomized rats
por: Urtado CB, et al.
Publicado: (2011) -
Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.
por: Arghya Datta, et al.
Publicado: (2021) - Steroids