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.

翻译：放射学报告通常以非结构化形式总结影像学检查的内容和解读，这种形式限制了定量分析。这导致放射学服务的监测仅限于内容无差异的通量评估，阻碍了针对特定目标的操作优化。本文提出Neuradicon——一种用于神经放射学报告定量分析的自然语言处理（NLP）框架。该框架采用规则模型与人工智能模型的混合方法，以简洁、定量的形式表征神经学报告，特别适用于操作指导。我们展示了Neuradicon在336,569份报告集的操作表型分析中的应用，并报告了其在时间维度及两个独立医疗机构中均具有卓越的泛化能力。