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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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| Publicado no: | J Am Coll Radiol |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7534384/ https://ncbi.nlm.nih.gov/pubmed/30600162 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jacr.2018.10.020 |
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