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Neuradicon: operational representation learning of neuroimaging reports

arXiv 2107.10021 ↗First posted 21 Jul 2021cs.CL cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 53 references that are in the map. First posted on arXiv 21 Jul 2021.

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10 of 53 references.

References · 10

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  1. 2025Building Models of Neurological Languagecited by 0