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Related Experiment Video

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Unsupervised entity and relation extraction from clinical records in Italian.

Anita Alicante1, Anna Corazza1, Francesco Isgrò1

  • 1Dipartimento di Ingegneria Elettrica e delle Tecnologie dell׳Informazione, DIETI, Università degli Studi di Napoli Federico II, Via Claudio, 21 - 80125 Napoli, Italy.

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This study introduces unsupervised text mining for Italian clinical records, extracting domain entities and discovering relations without labeled data. The approach shows promise for semi-automatic relation labeling.

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Computational Linguistics

Background:

  • Annotated clinical data is scarce and expensive for non-English languages.
  • Unsupervised methods are crucial for leveraging clinical text data effectively.
  • Information extraction from Italian clinical records presents unique challenges.

Purpose of the Study:

  • To propose and evaluate an unsupervised text mining system for information extraction from Italian clinical records.
  • To develop a two-step system for domain entity and relation extraction.
  • To investigate the performance of unsupervised clustering for relation discovery.

Main Methods:

  • Domain entities extracted using a metathesaurus and standard NLP tools.
  • Unsupervised clustering applied to entity pairs represented by feature vectors.
  • Automatic cluster labeling using significant features.
  • System evaluation on a large dataset of Italian clinical records.

Main Results:

  • The proposed unsupervised approach is effective for extracting information from Italian clinical records.
  • Clustering methods demonstrate potential for discovering relations between entities.
  • Different similarity measures impact performance, indicating areas for optimization.
  • The system is well-suited for semi-automatic labeling of extracted relations.

Conclusions:

  • Unsupervised text mining is a viable strategy for Italian clinical record analysis.
  • The developed system offers a promising solution for semi-automatic information extraction.
  • Further research can refine similarity measures and clustering techniques for improved performance.