Selective retrieval of pre- and post-coordinated SNOMED concepts.
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In general, it is very straightforward to store concept identifiers in electronic medical records and represent them in messages. Information models typically specify the fields that can contain coded entries. For each of these fields there may be additional constraints governing exactly which concept identifiers are applicable. However, because modern terminologies such as SNOMED CT are compositional, allowing concept expressions to be pre-coordinated within the terminology or post-coordinated within the medical record, there remains the potential to express a concept in more than one way. Often times, the various representations are similar, but not equivalent. This paper describes an approach for retrieving these pre- and post-coordinated concept expressions: (1) Create concept expressions using a logically-well-structured terminology (e.g., SNOMED CT) according to the rules of a well-specified information model (in this paper we use the HL7 RIM); (2) Transform pre- and post-coordinated concept expressions into a normalized form; (3) Transform queries into the same normalized form. The normalized instances can then be directly compared to the query. Several implementation considerations have been identified. Transformations into a normal form and execution of queries that require traversal of hierarchies need to be optimized. A detailed understanding of the information model and the terminology model are prerequisites. Queries based on the semantic properties of concepts are only as complete as the semantic information contained in the terminology model. Despite these considerations, the approach appears powerful and will continue to be refined. (+info)
An integrative model for in-silico clinical-genomics discovery science.
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Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes. (+info)
Improved coding of the primary reason for visit to the emergency department using SNOMED.
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There are over 100 million visits to emergency departments in the United States annually that could be a source of data for multiple uses including disease surveillance, health services research, quality assurance activates, and research. The patients' motivations for seeking care or the reason for visit (RFV) are recorded in every case. Efforts to utilize this rich source of data are hampered by inconsistent data entry and coding. This study analyzes ICD-9-CM, SNOMED-RT, and SNOMED-CT encoding of the RFV for accuracy. Each encoded reason for visit was compared to the text entry recorded at the time of visit to determine the closeness of fit. Each coded entry was judged to be an exact lexical match, a synonym, a broader or narrower concept or no match. SNOMED-CT was a lexical match or synonym for 93% of the text entries, while SNOMED-RT matched 87%, and ICD-9-CM matched 40%. We demonstrate that SNOMED coding of the RFV is more accurate than ICD-9-CM coding. (+info)
The lexical properties of the gene ontology.
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The Gene Ontology (GO) is a construct developed for the purpose of annotating molecular information about genes and their products. The ontology is a shared resource developed by the GO Consortium, a group of scientists who work on a variety of model organisms. In this paper we investigate the nature of the strings found in the Gene Ontology and evaluate them for their usefulness in natural language processing (NLP). We extend previous work that identified a set of properties that reliably identifies natural language phrases in the Unified Medical Language System (UMLS). The results indicate that a large percentage (79%) of GO terms are potentially useful for NLP applications. Some 35% of the GO terms were found in a corpus derived from the MEDLINE bibliographic database, and 27% of the terms were found in the current edition of the UMLS. (+info)
Role grouping as an extension to the description logic of Ontylog, motivated by concept modeling in SNOMED.
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Several clinical terminologies now utilize description logic to model the logical definitions of concepts. Recent editions of the Systematized Nomenclature of Medicine (SNOMED) have been developed using the description logic Ontylog. A significant design criterion for SNOMED is to keep concept expressions simple enough to be broadly usable by clinicians, while maintaining faithful representation of concept meaning. Motivated by this criterion, "role grouping" has been developed as an extension to the description logic Ontylog. This paper describes the problems that motivated the creation of role grouping, outlines the semantics of role grouping, illustrates the benefits of this construct with examples from SNOMED Clinical Terms, and provides an algorithm for determining normal forms for expressions involving role groups. (+info)
The SNOMED clinical terms development process: refinement and analysis of content.
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SNOMED Clinical Terms is a comprehensive concept-based health care terminology that was created by merging SNOMED RT and Clinical Terms Version 3. Following the mapping of concepts and descriptions into a merged database, the terminology was further refined by adding new content, modeling the relationships of individual concepts, and reviewing the hierarchical structure. A quality control process was performed to ensure integrity of the data. Additional features such as subsets, qualifiers, and mappings to other coding systems were added or updated to facilitate usability. We then analyzed the content of the completed work. This paper describes the refinement processes and compares the actual content of SNOMED CT with the early data obtained from analysis of the description mapping process. As predicted, the majority of concepts in SNOMED CT originated from SNOMED RT or CTV3, but not both. (+info)
Coverage of oncology drug indication concepts and compositional semantics by SNOMED-CT.
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OBJECTIVE: To evaluate SNOMED-CT 's ability to represent simple and compositional concepts in FDA approved oncology drug indications. METHODS: Oncology drug indications were decomposed into single and compositional concepts. SNOMED-CT's coverage of single concepts and the semantics needed to create compositional concepts were evaluated using automated and manual techniques. RESULTS: SNOMED-CT covered 86.3% of single concepts present in oncology drug indications; 11.3% of indications were covered completely. Coverage was best for concepts describing diseases, anatomy, and patient characteristics. Medications accounted for 50.5% of missing concepts. Excluding drug names, 45.2% of indications were completely represented. SNOMED-CT's semantics completely represented 60.1% of compositional expressions. CONCLUSIONS: SNOMED-CT's overall coverage of the concepts in oncology drug indications was good. Improvements or alternatives are needed for medications and semantics. (+info)
Putting data integration into practice: using biomedical terminologies to add structure to existing data sources.
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A major purpose of biomedical terminologies is to provide uniform concept representation, allowing for improved methods of analysis of biomedical information. While this goal is being realized in bioinformatics, with the emergence of the Gene Ontology as a standard, there is still no real standard for the representation of clinical concepts. As discoveries in biology and clinical medicine move from parallel to intersecting paths, standardized representation will become more important. A large portion of significant data, however, is mainly represented as free text, upon which conducting computer-based inferencing is nearly impossible. In order to test our hypothesis that existing biomedical terminologies, specifically the UMLS Metathesaurus and SNOMED CT, could be used as templates to implement semantic and logical relationships over free text data that is important both clinically and biologically, we chose to analyze OMIM (Online Mendelian Inheritance in Man). After finding OMIM entries' conceptual equivalents in each respective terminology, we extracted the semantic relationships that were present and evaluated a subset of them for semantic, logical, and biological legitimacy. Our study reveals the possibility of putting the knowledge present in biomedical terminologies to its intended use, with potentially clinically significant consequences. (+info)