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Related Experiment Video

Updated: Jul 9, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Advancing Italian biomedical information extraction with transformers-based models: Methodological insights and

Claudio Crema1, Tommaso Mario Buonocore2, Silvia Fostinelli3

  • 1Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.

Journal of Biomedical Informatics
|November 28, 2023
PubMed
Summary

This study introduces PsyNIT, an Italian neuropsychiatric Named Entity Recognition dataset, and a Transformer model to extract data from clinical notes. This advances automated information extraction in healthcare, particularly for under-resourced languages.

Keywords:
Biomedical text miningDeep learningLanguage modelNatural language processingTransformer

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Computational Linguistics

Background:

  • Computerized medical records reduce manual tasks but their data remains underutilized.
  • Extracting information from unstructured clinical text is time-consuming.
  • Automated text-mining pipelines can improve data accessibility.

Purpose of the Study:

  • To develop the first Italian neuropsychiatric Named Entity Recognition dataset (PsyNIT).
  • To create a Transformer-based model for information extraction from clinical notes.
  • To establish methodological guidelines for NLP in less-resourced languages.

Main Methods:

  • Creation of the PsyNIT dataset for Italian neuropsychiatric entities.
  • Development of a Transformers-based Named Entity Recognition model.
  • Implementation of a multicenter model using external datasets.

Main Results:

  • The developed multicenter model achieved an F1-score of 84.77%, Precision of 83.16%, and Recall of 86.44%.
  • Demonstrated the importance of consistent annotation and a "low-resource" fine-tuning strategy.
  • Successfully applied NLP techniques to unstructured clinical text.

Conclusions:

  • Automated information extraction can unlock the potential of clinical data.
  • Consistent annotation and adaptive fine-tuning are key for NLP model performance.
  • Methodological guidelines facilitate NLP research in languages with limited resources.