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A Concise Ontology to Support Research on Complex, Multimodal Clinical Reasoning

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The Semantic Web (ESWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13870))

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Abstract

When clinicians perform tasks involving clinical reasoning, such as the diagnosis or treatment of diabetes, multiple forms of reasoning, including deduction and abduction, are often employed. Ontologies designed to provide a foundation for clinical decision support systems have been encoded based on Clinical Practice Guidelines. Nevertheless, existing approaches solely allow deductive rules for clinical reasoning, with ontologies too large or complex to support tractable abductive reasoning. We follow existing guidelines and standards to design the Diabetes Pharmacology Ontology, a concise ontology – an ontology engineered by adhering to the Minimum Information to Reference an External Ontology Term principle and following an agile design approach. We claim that use cases that incorporate multiple forms of reasoning, such as those aimed at supporting both deduction and abduction, are better supported by concise, rather than complete and comprehensive, ontologies. We demonstrate how Personal Health Knowledge Graphs have been implemented using our ontology and evaluate the abductive capability of modules included with our ontology. We openly publish the resources that have resulted from this work, as listed below. This work demonstrates how multimodal semantic reasoning – deduction and abduction – can be used to emulate tasks involving clinical reasoning and thus has the potential to support practitioners with clinical decision-making.

Supported by Rensselaer Polytechnic Institute.

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Notes

  1. 1.

    For easy reference, we include this table as supplementary material: tetherless-world.github.io/diabetes-pharmacology-ontology/#supplementary-material.

  2. 2.

    https://bioportal.bioontology.org/ontologies/SYMP/.

  3. 3.

    https://github.com/tetherless-world/diabetes-pharmacology-ontology/tree/main/kb.

  4. 4.

    https://github.com/tetherless-world/diabetes-pharmacology-ontology/tree/main/ont/modules.

  5. 5.

    https://github.com/tetherless-world/diabetes-pharmacology-ontology/blob/main/abduction_commands.

  6. 6.

    https://tetherless-world.github.io/diabetes-pharmacology-ontology/#supplementary-material.

  7. 7.

    Available here: https://dai.fmph.uniba.sk/~pukancova/aaa/.

  8. 8.

    https://github.com/tetherless-world/diabetes-pharmacology-ontology/tree/main/evaluation_results.

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Acknowledgements

This work is supported by IBM Research AI through the AI Horizons Network. We would like to acknowledge Amar K. Das for his guidance. We also thank the members of the Tetherless World Constellation, especially Sola Shirai, Shruthi Chari, and Danielle Villa for their feedback.

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Authors and Affiliations

  1. Rensselaer Polytechnic Institute, Troy, NY, USA

    Sabbir M. Rashid, Jamie McCusker, Daniel Gruen, Oshani Seneviratne & Deborah L. McGuinness

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  1. Sabbir M. Rashid
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  2. Jamie McCusker
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  3. Daniel Gruen
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  4. Oshani Seneviratne
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  5. Deborah L. McGuinness
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Corresponding author

Correspondence to Sabbir M. Rashid .

Editor information

Editors and Affiliations

  1. Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  2. University of London, London, UK

    Ernesto Jimenez-Ruiz

  3. Rensselaer Polytechnic Institute, Troy, MI, USA

    Jamie McCusker

  4. Universidade de Lisboa, Lisbon, Portugal

    Daniel Faria

  5. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  6. KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  7. EURECOM, Biot, France

    Raphael Troncy

  8. University of Mannheim, Mannheim, Germany

    Sven Hertling

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Rashid, S.M., McCusker, J., Gruen, D., Seneviratne, O., McGuinness, D.L. (2023). A Concise Ontology to Support Research on Complex, Multimodal Clinical Reasoning. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_23

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  • DOI: https://doi.org/10.1007/978-3-031-33455-9_23

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