Improved diagnosis of thyroid cancer aided with deep learning applied to sonographic text reports: a retrospective, multi-cohort, diagnostic study

OBJECTIVE: Large volume radiological text data have been accumulated since the incorporation of electronic health record (EHR) systems in clinical practice. We aimed to determine whether deep natural language processing algorithms could aid radiologists in improving thyroid cancer diagnosis. METHODS...

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Detalles Bibliográficos
Autores principales: Zhang, Qiang, Zhang, Sheng, Li, Jianxin, Pan, Yi, Zhao, Jing, Feng, Yixing, Zhao, Yanhui, Wang, Xiaoqing, Zheng, Zhiming, Yang, Xiangming, Liu, Lixia, Qin, Chunxin, Zhao, Ke, Liu, Xiaonan, Li, Caixia, Zhang, Liuyang, Yang, Chunrui, Zhuo, Na, Zhang, Hong, Liu, Jie, Gao, Jinglei, Di, Xiaoling, Meng, Fanbo, Ji, Wei, Yang, Meng, Xin, Xiaojie, Wei, Xi, Jin, Rui, Zhang, Lun, Wang, Xudong, Song, Fengju, Zheng, Xiangqian, Gao, Ming, Chen, Kexin, Li, Xiangchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Compuscript 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9196053/
https://www.ncbi.nlm.nih.gov/pubmed/34491007
http://dx.doi.org/10.20892/j.issn.2095-3941.2020.0509

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9196053/
https://www.ncbi.nlm.nih.gov/pubmed/34491007
http://dx.doi.org/10.20892/j.issn.2095-3941.2020.0509

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