An Electronic Medical Record–Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning

BACKGROUND: Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm’s diagnostic accuracy will b...

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Detalles Bibliográficos
Autores principales: Greenstein, Alexander S., Teitel, Jack, Mitten, David J., Ricciardi, Benjamin F., Myers, Thomas G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567055/
https://www.ncbi.nlm.nih.gov/pubmed/33088883
http://dx.doi.org/10.1016/j.artd.2020.08.007

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567055/
https://www.ncbi.nlm.nih.gov/pubmed/33088883
http://dx.doi.org/10.1016/j.artd.2020.08.007

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