Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.
BACKGROUND
The secondary use of electronic healthcare records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, virtual health records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data.


OBJECTIVE
To develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies.


METHODS
We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (ie, data level) and the rest using ontologies (ie, knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning.


RESULTS
We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies, and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data have been used and the patients have been successfully classified by the risk of developing colorectal cancer.


CONCLUSIONS
This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies, and classification rules can be designed.
Departamento de Informática y Sistemas, Universidad de Murcia, Murcia, Spain.
Jesualdo Tomás
JT
Fernández-Breis
José Alberto
JA
Maldonado
Mar
M
Marcos
María del Carmen
Mdel C
Legaz-García
David
D
Moner
Joaquín
J
Torres-Sospedra
Angel
A
Esteban-Gil
Begoña
B
Martínez-Salvador
Montserrat
M
Robles
D000465:Algorithms; D064229:Biological Ontologies; D015331:Cohort Studies; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000592:standards; D006801:Humans; D020407:Internet; D010641:Phenotype; D012660:Semantics
Decision Support Systems, Clinical; Electronic Health Records/standards*; Medical Informatics; Semantics*