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September 10, 2025

Exploring Named Entity Recognition Potential and the Value of Tailored Natural Language Processing Pipelines for Radiology, Pathology, and Progress Notes in Clinical Decision Support: Quantitative Study (Preprint)

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Quantitative study shows improved NER precision in care notes for clinical decision support, indicating tailored approaches are essential.

The study found excellent named entity recognition precision, achieving a peak in procedures at 0.989.
NER assertion models demonstrated accuracy of 0.889 against manual annotations, highlighting effective data curation.
Progress care notes contained significantly more entities per sentence than radiology and pathology notes, with notable differences of 4-fold and 16-fold.
Further research is recommended to explore linguistic characteristics of various clinical notes, suggesting specialized NER models could enhance performance.

Kocaman et al. (2024) studied this question.

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Quantitative study shows improved NER precision in care notes for clinical decision support, indicating tailored approaches are essential.