Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

BACKGROUND: Identifying patients at risk of not achieving meaningful gains in long-term postsurgical patient-reported outcome measures (PROMs) is important for improving patient monitoring and facilitating presurgical decision support. Machine learning may help automatically select and weigh many pr...

Descrición completa

Gardado en:
Detalles Bibliográficos
Publicado en:Clin Orthop Relat Res
Main Authors: Fontana, Mark Alan, Lyman, Stephen, Sarker, Gourab K., Padgett, Douglas E., MacLean, Catherine H.
Formato: Artigo
Idioma:Inglês
Publicado: Wolters Kluwer 2019
Assuntos:
Regular Features
Acceso en liña:https://ncbi.nlm.nih.gov/pmc/articles/PMC6554103/
https://ncbi.nlm.nih.gov/pubmed/31094833
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1097/CORR.0000000000000687
Tags: Engadir etiqueta
Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!

Títulos similares