Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports

PURPOSE: Assess follow-up recommendations in radiology reports, develop and assess traditional machine learning (TML) and deep learning (DL) models in identifying follow-up, and benchmark them against a natural language processing (NLP) system. METHODS: This HIPAA-compliant, IRB approved study, was...

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Detalles Bibliográficos
Autores principales: Carrodeguas, E, Lacson, R, Swanson, W, Khorasani, R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534384/
https://www.ncbi.nlm.nih.gov/pubmed/30600162
http://dx.doi.org/10.1016/j.jacr.2018.10.020

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534384/
https://www.ncbi.nlm.nih.gov/pubmed/30600162
http://dx.doi.org/10.1016/j.jacr.2018.10.020

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