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Relation Extraction from Clinical Cases for a Knowledge Graph

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New Trends in Database and Information Systems (ADBIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1652))

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Abstract

We describe a system for automatic extraction of semantic relations between entities in a medical corpus of clinical cases. It builds upon a previously developed module for entity extraction and upon a morphosyntactic parser. It uses experimentally designed rules based on syntactic dependencies and trigger words, as well as on sequencing and nesting of entities of particular types. The results obtained on a small corpus are promising. Our larger perspective is transforming information extracted from medical texts into knowledge graphs.

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Notes

  1. 1.

    Extract from PubMed: https://pubmed.ncbi.nlm.nih.gov/35365471/.

  2. 2.

    https://www.slideshare.net/lyonwj/natural-language-processing-with-graph-databases-and-neo4j.

  3. 3.

    Conditional Random Fields [15] are probabilistic models often used in NLP for sequence labelling tasks as they take into account the context of the samples to label.

  4. 4.

    Entity types are listed in French and with the English translation, if it differs from French. In the rest of the paper, the English names are used in the core of the texts, while the French ones appear in figures. The entity types substance, anatomy and treatment will sometimes be abbreviated by sub, anat and treat, respectively.

  5. 5.

    https://spacy.io/.

  6. 6.

    https://spacy.io/models/fr.

  7. 7.

    https://github.com/UniversalDependencies/UD_French-Sequoia.

  8. 8.

    Word \(w_1\) is a syntactic head of word \(w_2\) if there is a syntactic dependency link outgoing from \(w_1\) and incoming in \(w_2\). Most dependency parsing models ensure that each word (except the root of the sentence) has exactly one head, i.e. the dependency graph is a tree.

  9. 9.

    An asterisk following an entity type signals a negated entity occurrence.

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Acknowledgements

Work partly supported by the ICVL federation and RTR-DIAMS. It is done in the context of DOING action of the GDR-MADICS.

Author information

Authors and Affiliations

  1. LISN, Paris-Saclay University, CNRS, Orsay, France

    Agata Savary

  2. LIFO, Université d’Orléans, INSA CVL, Orléans, France

    Alena Silvanovich, Nicolas Hiot & Mirian Halfeld Ferrari

  3. LLL, Université d’Orléans, Orléans, France

    Anne-Lyse Minard

  4. EnnovLabs – Ennov, Paris, France

    Nicolas Hiot

Authors
  1. Agata Savary
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  2. Alena Silvanovich
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  3. Anne-Lyse Minard
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  4. Nicolas Hiot
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  5. Mirian Halfeld Ferrari
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Corresponding author

Correspondence to Agata Savary .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  5. University of Genoa, Genoa, Italy

    Barbara Catania

  6. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas-Solar

  7. University of Calabria, Rende, Italy

    Ester Zumpano

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Savary, A., Silvanovich, A., Minard, AL., Hiot, N., Halfeld Ferrari, M. (2022). Relation Extraction from Clinical Cases for a Knowledge Graph. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_33

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  • DOI: https://doi.org/10.1007/978-3-031-15743-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15742-4

  • Online ISBN: 978-3-031-15743-1

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