Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting the Risk of Surgical Site Infection Following Minimally Invasive Transforaminal Lumbar Interbody Fusion

Purpose: Machine Learning (ML) is rapidly growing in capability and is increasingly applied to model outcomes and complications in medicine. Surgical site infections (SSI) are a common post-operative complication in spinal surgery. This study aimed to develop and validate supervised ML algorithms fo...

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Detalles Bibliográficos
Autores principales: Wang, Haosheng, Fan, Tingting, Yang, Bo, Lin, Qiang, Li, Wenle, Yang, Mingyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8720930/
https://www.ncbi.nlm.nih.gov/pubmed/34988091
http://dx.doi.org/10.3389/fmed.2021.771608

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8720930/
https://www.ncbi.nlm.nih.gov/pubmed/34988091
http://dx.doi.org/10.3389/fmed.2021.771608

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