RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports.

OBJECTIVES

Radiology reports contain valuable information for research and audits, but relevant details are often buried within free-text fields. This makes them challenging and time-consuming to extract for secondary analyses, including training artificial intelligence (AI) models.

MATERIALS AND METHODS

This study presents a rule-based RAdiology Data EXtraction tool (RADEX) to enable biomedical researchers and healthcare professionals to automate information extraction from clinical documents. RADEX simplifies the translation of domain expertise into regular-expression models, enabling context-dependent searching without specialist expertise in Natural Language Processing. Its utility was demonstrated in the multi-label classification of fourteen clinical features in a large retrospective dataset (n = 16,246) of thyroid ultrasound reports from five hospitals in the United Kingdom (UK). A tuning subset (n = 200) was used to iteratively develop the search strategy, and a holdout test subset (n = 202) was used to evaluate the performance against reference-standard labels.

RESULTS

The dataset cardinality was 3.06, and the label density was 0.34. Cohen's Kappa was 0.94 for rater 1 and 0.95 for rater 2. For RADEX, micro-average sensitivity, specificity, and F1-score were 0.97, 0.96, and 0.94, respectively. The processing time was 12.3 milliseconds per report, enabling fast and reliable information extraction.

CONCLUSION

RADEX is a versatile tool for bespoke research and audit applications, where access to labelled data or computing infrastructure is limited, or explainability and reproducibility are priorities. This offers a time-saving and freely available option to accelerate structured data collection, enabling new insights and improved patient care.

KEY POINTS

Question Radiology reports contain vital information that is buried in unstructured free-text fields. Can we extract this information effectively for research and audit applications? Findings A rule-based RAdiology Data Extraction tool (RADEX) is described and used to classify fourteen key findings from thyroid ultrasound reports with sensitivity and specificity > 0.95. Clinical relevance RADEX offers clinicians and researchers a time-saving tool to accelerate structured data collection. This practical approach prioritises transparency, repeatability, and usability, enabling new insights into improved patient care.