Ontology Based Medical Diagnosis Decision Support System (original) (raw)
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Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can th...
In recent years, there is an increasing demand for building interoperable information systems. Interoperability is crucial in the field of Health Care because the sharing of information may be essential to ensure a good treatment for the patient. The Clinical Evolution Record (CER) is an example of that, since it consists of a large document where we can find temporal information related to the whole history of patient's health conditions and medical procedures, exams, internments and treatments, among others, as part of the Electronic Patient Record (EPR). Aiming at providing solid and wide interoperability to CER information, in this article we propose an ontology based on a clinical data structure built in a previous work. To ensure the semantic interoperability we use the UMLS (Unified Medical Language System) Semantic Network as an upper-level ontology, so that the proposed ontology works as an extension of it.
2009 22nd IEEE International Symposium on Computer-Based Medical Systems, 2009
Providing semantic interoperability is a current challenge in the field of data integration. In healthcare environments, sharing information may be essential to ensure a good treatment to the patient. In addition, it is necessary to ensure the accuracy of the data that is being exchanged. In this paper we present the design and implementation of an ontology for the patient clinical evolution record, using the UMLS Semantic Network as an upper-level ontology, based on a clinical data structure. We show how our ontology can be used as a semantic connection between two distinct health databases in their data integration process.