Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements

BACKGROUND: The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major po...

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Detalles Bibliográficos
Autores principales: Devana, Sai K, Shah, Akash A, Lee, Changhee, Jensen, Andrew R, Cheung, Edward, van der Schaar, Mihaela, SooHoo, Nelson F
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Original Scientific Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9163721/
https://www.ncbi.nlm.nih.gov/pubmed/35669619
http://dx.doi.org/10.1177/24715492221075444

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9163721/
https://www.ncbi.nlm.nih.gov/pubmed/35669619
http://dx.doi.org/10.1177/24715492221075444

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