Cargando…
Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study
BACKGROUND: Surgical site infection (SSI) is one of the most common types of health care–associated infections. It increases mortality, prolongs hospital length of stay, and raises health care costs. Many institutions developed risk assessment models for SSI to help surgeons preoperatively identify...
Autores principales: | Chen, Weijia, Lu, Zhijun, You, Lijue, Zhou, Lingling, Xu, Jie, Chen, Ken |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325005/ https://www.ncbi.nlm.nih.gov/pubmed/32538798 http://dx.doi.org/10.2196/18186 |
Ejemplares similares
-
Integrated multimodal artificial intelligence framework for healthcare applications
por: Soenksen, Luis R., et al.
Publicado: (2022) -
Editorial: Application of multimodal imaging combined with artificial intelligence in eye diseases
por: Wen, Zhi, et al.
Publicado: (2023) -
Towards artificial general intelligence via a multimodal foundation model
por: Fei, Nanyi, et al.
Publicado: (2022) -
Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study
por: Zhang, Huiyong, et al.
Publicado: (2023) -
Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm
por: Wang, Bin, et al.
Publicado: (2021)