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J Biomed Semantics. 2017 Feb 7;8(1):6. doi: 10.1186/s13326-017-0114-4.

Building a model for disease classification integration in oncology, an approach based on the national cancer institute thesaurus.

Author information

1
CHU de Bordeaux, Pole de sante publiqueService d'information medicale, unit IAM, F-33000Bordeaux, France. vianney.jouhet@isped.u-bordeaux.fr.
2
Univ. Bordeaux, Inserm, UMR 1219, Bordeaux, F-33000, France. vianney.jouhet@isped.u-bordeaux.fr.
3
Univ. Bordeaux, Inserm, UMR 1219, Bordeaux, F-33000, France.
4
CHU de Bordeaux, Pole de sante publiqueService d'information medicale, unit IAM, F-33000Bordeaux, France.

Abstract

BACKGROUND:

Identifying incident cancer cases within a population remains essential for scientific research in oncology. Data produced within electronic health records can be useful for this purpose. Due to the multiplicity of providers, heterogeneous terminologies such as ICD-10 and ICD-O-3 are used for oncology diagnosis recording purpose. To enable disease identification based on these diagnoses, there is a need for integrating disease classifications in oncology. Our aim was to build a model integrating concepts involved in two disease classifications, namely ICD-10 (diagnosis) and ICD-O-3 (topography and morphology), despite their structural heterogeneity. Based on the NCIt, a "derivative" model for linking diagnosis and topography-morphology combinations was defined and built. ICD-O-3 and ICD-10 codes were then used to instantiate classes of the "derivative" model. Links between terminologies obtained through the model were then compared to mappings provided by the Surveillance, Epidemiology, and End Results (SEER) program.

RESULTS:

The model integrated 42% of neoplasm ICD-10 codes (excluding metastasis), 98% of ICD-O-3 morphology codes (excluding metastasis) and 68% of ICD-O-3 topography codes. For every codes instantiating at least a class in the "derivative" model, comparison with SEER mappings reveals that all mappings were actually available in the model as a link between the corresponding codes.

CONCLUSIONS:

We have proposed a method to automatically build a model for integrating ICD-10 and ICD-O-3 based on the NCIt. The resulting "derivative" model is a machine understandable resource that enables an integrated view of these heterogeneous terminologies. The NCIt structure and the available relationships can help to bridge disease classifications taking into account their structural and granular heterogeneities. However, (i) inconsistencies exist within the NCIt leading to misclassifications in the "derivative" model, (ii) the "derivative" model only integrates a part of ICD-10 and ICD-O-3. The NCIt is not sufficient for integration purpose and further work based on other termino-ontological resources is needed in order to enrich the model and avoid identified inconsistencies.

KEYWORDS:

ICD-10; ICD-O-3; NCI thesaurus; Oncology; Semantic integration; Terminology

PMID:
28173841
PMCID:
PMC5294908
DOI:
10.1186/s13326-017-0114-4
[Indexed for MEDLINE]
Free PMC Article

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