Unified Medical Language System Semantic Network Research Articles

UMLS Semantic Network as a UML Metamodel for Improving Biomedical Ontology and Application Modeling

In the domain of biomedical and health informatics, ontologies are widely used to capture knowledge ranging from bioinformatics such as gene, protein, protein interactions, etc. to clinical/healthcare informatics knowledge such as diseases, symptoms, treatment, medication, etc. Currently, one medical knowledge source that encapsulates a broad spectrum of medical knowledge is the Unified Medical Language System (UMLS), which can be defined as a compendium of diverse medical ontological standards. The primary components of the UMLS are: Semantic Network (UMLS-SN) – designed by interconnecting well-defined semantic types with semantic relationships, and Metathesaurus (UMLS-META) – the base of UMLS system that is comprised of millions of medical concepts from diverse medical standards. However, within the biomedical and health informatics community, the concepts of software engineering and domain modeling (using meta-models such as ERD, UML, and XML) are very successful in designing and implementing biomedical/health domain application models. In the current status, the UMLS-SN is primarily employed for classification of medical concepts in UMLS-META, but UMLS-SN knowledge can't be viewed or employed as a modeling framework for designing ontological models and is restricted to the UMLS environment. Thus, the impact of the biomedical semantics captured by UMLS-SN might be minimal in medical facilities, research and healthcare organizations that are highly influenced by software engineering, meta-models and domain model-based practices. In order fill this gap, the authors propose a meta-modeling framework for UMLS-SN based on the UML Profile (built using UML meta-model) that will result in a customized domain specific meta-model. This specialized meta-model that encapsulates the medical knowledge semantics of UMLS-SN can then be employed for designing ontological models or relevant healthcare application models and simultaneously be coherent with software meta-models and domain modeling practices.

A chemical specialty semantic network for the Unified Medical Language System.

BackgroundTerms representing chemical concepts found the Unified Medical Language System (UMLS) are used to derive an expanded semantic network with mutually exclusive semantic types. The UMLS Semantic Network (SN) is composed of a collection of broad categories called semantic types (STs) that are assigned to concepts. Within the UMLS’s coverage of the chemical domain, we find a great deal of concepts being assigned more than one ST. This leads to the situation where the extent of a given ST may contain concepts elaborating variegated semantics.A methodology for expanding the chemical subhierarchy of the SN into a finer-grained categorization of mutually exclusive types with semantically uniform extents is presented. We call this network a Chemical Specialty Semantic Network (CSSN). A CSSN is derived automatically from the existing chemical STs and their assignments. The methodology incorporates a threshold value governing the minimum size of a type’s extent needed for inclusion in the CSSN. Thus, different CSSNs can be created by choosing different threshold values based on varying requirements.ResultsA complete CSSN is derived using a threshold value of 300 and having 68 STs. It is used effectively to provide high-level categorizations for a random sample of compounds from the “Chemical Entities of Biological Interest” (ChEBI) ontology. The effect on the size of the CSSN using various threshold parameter values between one and 500 is shown.ConclusionsThe methodology has several potential applications, including its use to derive a pre-coordinated guide for ST assignments to new UMLS chemical concepts, as a tool for auditing existing concepts, inter-terminology mapping, and to serve as an upper-level network for ChEBI.

Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups

ObjectiveThe objective of this study is to develop an approach to evaluate the quality of terminological annotations on the value set (ie, enumerated value domain) components of the common data elements (CDEs) in the context of clinical research using both unified medical language system (UMLS) semantic types and groups.Materials and methodsThe CDEs of the National Cancer Institute (NCI) Cancer Data Standards Repository, the NCI Thesaurus (NCIt) concepts and the UMLS semantic network were integrated using a semantic web-based framework for a SPARQL-enabled evaluation. First, the set of CDE-permissible values with corresponding meanings in external controlled terminologies were isolated. The corresponding value meanings were then evaluated against their NCI- or UMLS-generated semantic network mapping to determine whether all of the meanings fell within the same semantic group.ResultsOf the enumerated CDEs in the Cancer Data Standards Repository, 3093 (26.2%) had elements drawn from more than one UMLS semantic group. A random sample (n=100) of this set of elements indicated that 17% of them were likely to have been misclassified.DiscussionThe use of existing semantic web tools can support a high-throughput mechanism for evaluating the quality of large CDE collections. This study demonstrates that the involvement of multiple semantic groups in an enumerated value domain of a CDE is an effective anchor to trigger an auditing point for quality evaluation activities.ConclusionThis approach produces a useful quality assurance mechanism for a clinical study CDE repository.

Modeling Multi-typed Structurally Viewed Chemicals with the UMLS Refined Semantic Network

ObjectiveChemical concepts assigned multiple “Chemical Viewed Structurally” semantic types (STs) in the Unified Medical Language System (UMLS) are subject to ambiguous interpretation. The multiple assignments may denote the fact that a specific represented chemical (combination) is a conjugate, derived via a chemical reaction of chemicals of the different types, or a complex, composed of a mixture of such chemicals. The previously introduced Refined Semantic Network (RSN) is modified to properly model these varied multi-typed chemical combinations. DesignThe RSN was previously introduced as an enhanced abstraction of the UMLS's concepts. It features new types, called intersection semantic types (ISTs), each of which explicitly captures a unique combination of ST assignments in one abstract unit. The ambiguous ISTs of different “Chemical Viewed Structurally” ISTs of the RSN are replaced with two varieties of new types, called conjugate types and complex types, which explicitly denote the nature of the chemical interactions. Additional semantic relationships help further refine that new portion of the RSN rooted at the ST “Chemical Viewed Structurally.” MeasurementsThe number of new conjugate and complex types and the amount of changes to the type assignment of chemical concepts are presented. ResultsThe modified RSN, consisting of 35 types and featuring 22 new conjugate and complex types, is presented. A total of 800 (about 98%) chemical concepts representing multi-typed chemical combinations from “Chemical Viewed Structurally” STs are uniquely assigned one of the new types. An additional benefit is the identification of a number of illegal ISTs and ST assignment errors, some of which are direct violations of exclusion rules defined by the UMLS Semantic Network. ConclusionThe modified RSN provides an enhanced abstract view of the UMLS's chemical content. Its array of conjugate and complex types provides a more accurate model of the variety of combinations involving chemicals viewed structurally. This framework will help streamline the process of type assignments for such chemical concepts and improve user orientation to the richness of the chemical content of the UMLS.

Partitioning the UMLS semantic network.

The unified medical language system (UMLS) integrates many well-established biomedical terminologies. The UMLS semantic network (SN) can help orient users to the vast knowledge content of the UMLS Metathesaurus (META) via its abstract conceptual view. However, the SN itself is large and complex and may still be difficult to comprehend. Our technique partitions the SN into smaller meaningful units amenable to display on limited-sized computer screens. The basis for the partitioning is the distribution of the relationships within the SN. Three rules are applied to transform the original partition into a second more cohesive partition.