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Diagnosis Ranking with Knowledge Graph Convolutional Networks

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Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12656))

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Abstract

The automatic diagnosis of a medical condition provided the symptoms exhibited by a patient is at the basis of systems for clinical decision support, as well as for applications such as symptom checkers. Existing methods have not fully exploited medical knowledge: this likely hinders their effectiveness. In this work, we propose a knowledge-aware diagnosis ranking framework based on medical knowledge graph (KG) and graph convolutional neural network (GCN). The medical KG is used to model hierarchy and causality relationships between diseases and symptoms. We have evaluated our proposed method using realistic patient cases. The empirical results show that our knowledge-aware diagnosis ranking framework can improve the effectiveness of medical diagnosis.

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Notes

  1. 1.

    https://www.meddra.org/.

  2. 2.

    Note that the relation causes in SemmedDB is rather coarse and encompasses relations that would normally be treated as separate in other medical KGs, including relations such as has_complication, has_symptom.

  3. 3.

    We link a patient with the KG through the symptoms’ Concept Unique Identifiers (CUIs). Medical concept recognition tools like QuickUMLS [22] and MetaMap [2] can recognize and map terms in patients’ records to CUIs; each entity in the medical KG is represented by a CUI.

  4. 4.

    https://mbr.nlm.nih.gov/Download/MetaMapped_Medline/2019/MMO/.

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Acknowledgements

This research is supported by the Shenyang Science and Technology Plan Fund (No. 20-201-4-10), the Member Program of Neusoft Research of Intelligent Healthcare Technology, Co. Ltd. (No. NRMP001901)). A/Prof Guido Zuccon is the recipient of an Australian Research Council DECRA Research Fellowship (DE180101579) and a Google Faculty Award.

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Authors and Affiliations

  1. The University of Queensland, St. Lucia, Brisbane, Australia

    Bing Liu, Guido Zuccon, Wen Hua & Weitong Chen

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  1. Bing Liu
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  2. Guido Zuccon
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Corresponding author

Correspondence to Bing Liu .

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Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

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Liu, B., Zuccon, G., Hua, W., Chen, W. (2021). Diagnosis Ranking with Knowledge Graph Convolutional Networks. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_24

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  • DOI: https://doi.org/10.1007/978-3-030-72113-8_24

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