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Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning Approach

2025-12-12

Abstract excerpt

<h4>Background: </h4> Anatomopathological reports remain predominantly unstructured within Electronic Medical Records, limiting automated data extraction, interoperability between healthcare institutions, and large-scale clinical research applications. Manual entity extraction and standardization processes are inconsistent, costly, and insufficiently scalable for modern healthcare systems. <h4>Aim:</h4> Our study...

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Literature Corpus work
319d7363-f02b-55cc-ad1e-0908d86ebf3c
DOI
10.20944/preprints202512.1075.v1
Open publication

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Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning ApproachDOI 10.20944/preprints202512.1075.v1
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