Neuradicon: operational representation learning of neuroimaging reports

TL;DR

Neuradicon is an NLP framework that converts unstructured neuroradiological reports into quantitative data, enabling better operational analysis and monitoring across healthcare institutions.

Contribution

The paper introduces Neuradicon, a hybrid rule-based and AI NLP framework for quantitative analysis of radiology reports, improving operational insights.

Findings

Effective generalization across time and institutions

Successful application to large report corpus

Enhanced operational phenotyping capabilities

Abstract

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.