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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2107.10021 |
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| _version_ | 1866910788345659392 |
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| author | Watkins, Henry Gray, Robert Julius, Adam Mah, Yee-Haur Pinaya, Walter H. L. Wright, Paul Jha, Ashwani Engleitner, Holger Cardoso, Jorge Ourselin, Sebastien Rees, Geraint Jaeger, Rolf Nachev, Parashkev |
| author_facet | Watkins, Henry Gray, Robert Julius, Adam Mah, Yee-Haur Pinaya, Walter H. L. Wright, Paul Jha, Ashwani Engleitner, Holger Cardoso, Jorge Ourselin, Sebastien Rees, Geraint Jaeger, Rolf Nachev, Parashkev |
| contents | Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of radiological services to throughput undifferentiated by content, impeding specific, targeted operational optimization. Here we present Neuradicon, a natural language processing (NLP) framework for quantitative analysis of neuroradiological reports. Our framework is a hybrid of rule-based and artificial intelligence models to represent neurological reports in succinct, quantitative form optimally suited to operational guidance. We demonstrate the application of Neuradicon to operational phenotyping of a corpus of 336,569 reports, and report excellent generalizability across time and two independent healthcare institutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_10021 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Neuradicon: operational representation learning of neuroimaging reports Watkins, Henry Gray, Robert Julius, Adam Mah, Yee-Haur Pinaya, Walter H. L. Wright, Paul Jha, Ashwani Engleitner, Holger Cardoso, Jorge Ourselin, Sebastien Rees, Geraint Jaeger, Rolf Nachev, Parashkev Computation and Language Artificial Intelligence Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of radiological services to throughput undifferentiated by content, impeding specific, targeted operational optimization. Here we present Neuradicon, a natural language processing (NLP) framework for quantitative analysis of neuroradiological reports. Our framework is a hybrid of rule-based and artificial intelligence models to represent neurological reports in succinct, quantitative form optimally suited to operational guidance. We demonstrate the application of Neuradicon to operational phenotyping of a corpus of 336,569 reports, and report excellent generalizability across time and two independent healthcare institutions. |
| title | Neuradicon: operational representation learning of neuroimaging reports |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2107.10021 |