Evidence map›Paper›PMID 28173841›Full record

ArticleJournal of biomedical semantics2017

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

Vianney Jouhet, Fleur Mougin, Bérénice Bréchat, Frantz Thiessard

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Assigning ICD-O-3 Codes to Pathology Reports using Neural Multi-Task Training with Hierarchical Regularization.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2021
    Article
  5. Article
  6. Development of a cardiac-centered frailty ontology.Journal of biomedical semantics · 2019
    Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Vianney JouhetCHU de Bordeaux, Pole de sante publiqueService d'information medicale, unit IAM, F-33000Bordeaux, France. vianney.jouhet@isped.u-bordeaux.fr.
Fleur MouginUniv. Bordeaux, Inserm, UMR 1219, Bordeaux, F-33000, France.
Bérénice BréchatCHU de Bordeaux, Pole de sante publiqueService d'information medicale, unit IAM, F-33000Bordeaux, France.
Frantz ThiessardCHU de Bordeaux, Pole de sante publiqueService d'information medicale, unit IAM, F-33000Bordeaux, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying 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.

resultsThe 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.

conclusionsWe 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.

Indexed as

International Classification of DiseasesNational Cancer Institute (U.S.)HumansNeoplasmsUnited StatesICD-10ICD-O-3NCI thesaurusOncologySemantic integrationTerminology

Identifiers

PMID28173841
PMCPMC5294908

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.