Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning Approach
Rattachement africain : Tunisie, tr, ca, it, gb. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Background: 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.Aim: Our study aimed to (i) develop a domain-specific Named Entity Recognition model using BioBERT for extracting sample type, test performed, and finding entities from anatomopathological reports; (ii) implement a hybrid standardization framework combining BioClinicalBERT classification with Retrieval-Augmented Generation to map entities to SNOMED CT, LOINC, and ICD-11 terminologies; and (iii) evaluate the performance of this pipeline on real-world clinical reports. Methods: We manually annotated 560 anatomopathological reports from the Military Hospital of Tunis, establishing a gold-standard corpus. The pipeline integrated BioBERT v1.1 for entity extraction, trained for three epochs with the AdamW optimizer at a learning rate of 2×10⁻⁵, a batch size of 8, and weight decay of 0.01. Standardization employed BioClinicalBERT for multi-label classification, augmented by dense vector retrieval from official SNOMED CT, LOINC, and ICD-11 databases. Performance evaluation utilized precision, recall, and F1-score metrics with an 80-20 train-test split. Results: BioBERT achieved F1-scores of 0.97 for sample type, 0.98 for test performed, and 0.93 for finding entities, with overall precision of 0.969 and recall of 0.958. Bootstrap-estimated 95% confidence intervals confirmed robust performance stability. Absolute error analysis revealed 45 misclassified tokens in the test (relative error 6.9%) and six tokens in the finding (relative error 1%). One-sample t-tests yielded t-values of 15.71 for recall and 30.24 for F1-score, with all p-values below 0.0001. The hybrid standardization framework demonstrated F1-macro scores of 0.6159 for SNOMED CT, 0.9294 for LOINC, and 0.7201 for ICD-11 mapping. Cohen’s Kappa values ranged from 0.6871 to 0.9773 across ontologies. Statistical comparison between BioClinicalBERT and Fusion/Reranker models showed McNemar test p-values exceeding 0.370 and permutation test p-values ranging from 0.375 to 0.625. Conclusion: This study demonstrates that transformer-based Named Entity Recognition combined with retrieval-augmented standardization achieves clinically validated performance for automated extraction and multi-ontology coding of anatomopathological entities. Multi-institutional validation studies are necessary to assess generalizability before clinical deployment.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning Approach
- Date Crossref
- 12/12/2025
- Éditeur
- MDPI AG
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Tunis El Manar University Tunis El Manar University, Tunisie (code pays fourni par la source)Université ou école supérieure
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Manouba University Manouba University, Tunisie (code pays fourni par la source)Université ou école supérieure
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Military Hospital of Tunis Military Hospital of Tunis, Tunisie (code pays fourni par la source)Établissement de santé
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Atatürk University pays non établi dans la noticeUniversité ou école supérieure
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York University Laboratory for Industrial and Applied Mathematics (LIAM) pays non établi dans la noticeUniversité ou école supérieure
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Accademia di Belle Arti di Napoli pays non établi dans la noticeUniversité ou école supérieure
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Artificial Engineering (Italy) pays non établi dans la noticeEntreprise
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University of the West of England The Computer Science Research Centre pays non établi dans la noticeUniversité ou école supérieure
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University of Tunis El Manar Laboratory of Biophysics and Medical Technologies Tunis, Tunisie (pays nommé en fin d’affiliation)Université ou école supérieure
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University of Manouba Higher Institute of Sport and Physical Education of Ksar Said University of Manouba, Tunisie (pays nommé en fin d’affiliation)Université ou école supérieure
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Faculty of Medicine of Tunis Department of Pathology Military Hospital of Tunis, Tunisie (pays nommé en fin d’affiliation)Université ou école supérieure
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Faculty of Sports Sciences Physical Education of Sports Teaching Department pays non établi dans la noticeUniversité ou école supérieure
Tunis El Manar University (Tunis El Manar University, Tunisie), Manouba University (Manouba University, Tunisie) et Military Hospital of Tunis (Military Hospital of Tunis, Tunisie), avec 9 autres affiliations. Pays d’affiliation : Tunisie.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.