Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing.
We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for...
| Publicado en: | Journal of Digital Imaging Vol. 30; no. 3; pp. 314 - 323 |
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| Autores principales: | , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2017
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| Acceso en línea: | Ver este registro en EBSCOhost |