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Defining the distance between diseases using SNOMED CT embeddings.

Mingzhou Fu1, Yu Yan2, Loes M Olde Loohuis3

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Summary
This summary is machine-generated.

This study introduces a novel knowledge graph-based disease distance metric for all International Classification of Diseases, version 10 (ICD-10) codes. This metric enhances understanding of disease relationships for precision health applications.

Keywords:
Disease distanceElectronic health recordsICD-10 codesKnowledge graphSNOMED CT

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Medical Informatics

Background:

  • Understanding disease relationships is crucial for biomedical research, patient stratification, and clinical decision-making.
  • Existing disease distance metrics are limited to specific disease sets, hindering broader applications.
  • A comprehensive disease distance metric is needed for the full spectrum of diseases.

Purpose of the Study:

  • To define a novel disease distance metric for all International Classification of Diseases, version 10 (ICD-10) disease pairs.
  • To leverage the Systemized Nomenclature of Medicine, Clinical Terms (SNOMED CT) biomedical ontology as a knowledge graph for computing disease distances.
  • To compare the proposed knowledge graph-based metric against existing metrics based on ICD hierarchy, clinical comorbidity, and genetic correlation.

Main Methods:

  • Computed disease distances for all ICD-10 pairs using SNOMED CT as a knowledge graph.
  • Compared the knowledge graph-based metric with hierarchical ICD, clinical comorbidity, and genetic correlation metrics.
  • Evaluated the ability of each metric to capture disease relationships.

Main Results:

  • The proposed knowledge graph-based distance metric captures known phenotypic, clinical, and molecular disease characteristics.
  • This metric offers finer granularity in representing disease relationships compared to the other three metrics evaluated.
  • The metric demonstrates potential for advanced research applications using electronic health records.

Conclusions:

  • The developed knowledge graph-based disease distance metric provides a comprehensive approach to understanding disease relationships across the entire ICD-10 classification.
  • This metric is poised to significantly advance patient subgrouping for precision health, individualized disease prevention, and treatment strategies.
  • The metric's utility is expected to grow with the increasing use of electronic health records in research.