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Mapping of ICD-O Tuples to OncoTree Codes Using SNOMED CT Post-Coordination

Tessa Ohlsen*, Valerie Kruse, Rosemarie Krupar, Alexandra Banach, Josef Ingenerf, Cora Drenkhahn

*Korrespondierende/r Autor/-in für diese Arbeit

Abstract

Around 500,000 oncological diseases are diagnosed in Germany every year which are documented using the International Classification of Diseases for Oncology (ICD-O). Apart from this, another classification for oncology, OncoTree, is often used for the integration of new research findings in oncology. For this purpose, a semi-automatic mapping of ICD-O tuples to OncoTree codes was developed. The implementation uses a FHIR terminology server, pre-coordinated or post-coordinated SNOMED CT expressions, and subsumption testing. Various validations have been applied. The results were compared with reference data of scientific papers and manually evaluated by a senior pathologist, confirming the applicability of SNOMED CT in general and its post-coordinated expressions in particular as a viable intermediate mapping step. Resulting in an agreement of 84,00 % between the newly developed approach and the manual mapping, it becomes obvious that the present approach has the potential to be used in everyday medical practice.

OriginalspracheEnglisch
TitelVolume 294: Challenges of Trustable AI and Added-Value on Health
Erscheinungsdatum25.05.2022
PublikationsstatusVeröffentlicht - 25.05.2022

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

  1. SDG 3 – Gesundheit und Wohlergehen
    SDG 3 – Gesundheit und Wohlergehen
  2. SDG 9 – Industrie, Innovation und Infrastruktur
    SDG 9 – Industrie, Innovation und Infrastruktur

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