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Objective Controlled medical terminologies (CMTs) have been recognized as important tools in a variety of medical informatics applications, ranging from patient-record systems to decision-support systems. Controlled medical terminologies are typically organized in semantic network structures consisting of tens to hundreds of thousands of concepts. This overwhelming size and complexity can be a serious barrier to their maintenance and widespread utilization. The authors propose the use of object-oriented databases to address the problems posed by the extensive scope and high complexity of most CMTs for maintenance personnel and general users alike. Design The authors present a methodology that allows an existing CMT, modeled as a semantic network, to be represented as an equivalent object-oriented database. Such a representation is called an object-oriented health care terminology repository (OOHTR). Results The major benefit of an OOHTR is its schema, which provides an important layer of structural abstraction. Using the high-level view of a CMT afforded by the schema, one can gain insight into the CMT's overarching organization and begin to better comprehend it. The authors' methodology is applied to the Medical Entities Dictionary (MED), a large CMT developed at Columbia-Presbyterian Medical Center. Examples of how the OOHTR schema facilitated updating, correcting, and improving the design of the MED are presented. Conclusion The OOHTR schema can serve as an important abstraction mechanism for enhancing comprehension of a large CMT, and thus promotes its usability. [ABSTRACT FROM PUBLISHER]

Background: The UMLS assigns semantic types to all its integrated concepts. The semantic types are widely used in various natural language processing tasks in the biomedical domain, such as named entity recognition, semantic disambiguation, and semantic annotation. Due to the size of the UMLS, erroneous semantic type assignments are hard to detect. It is imperative to devise automated techniques to identify errors and inconsistencies in semantic type assignments., Objectives: Designing a methodology to perform programmatic checks to detect semantic type assignment errors for UMLS concepts with one or more SNOMED CT terms and evaluating concepts in a selected set of SNOMED CT hierarchies to verify our hypothesis that UMLS semantic type assignment errors may exist in concepts residing in semantically inconsistent groups., Methods: Our methodology is a four-stage process. 1) partitioning concepts in a SNOMED CT hierarchy into semantically uniform groups based on their assigned semantic tags; 2) partitioning concepts in each group from 1) into the disjoint sub-groups based on their semantic type assignments; 3) mapping all SNOMED CT semantic tags into one or more semantic types in the UMLS; 4) identifying semantically inconsistent groups that have inconsistent assignments between semantic tags and semantic types according to the mapping from 3) and providing concepts in such groups to the domain experts for reviewing., Results: We applied our method on the UMLS 2013AA release. Concepts of the semantically inconsistent groups in the PHYSICAL FORCE and RECORD ARTIFACT hierarchies have error rates 33% and 62.5% respectively, which are greatly larger than error rates 0.6% and 1% in semantically consistent groups of the two hierarchies., Conclusion: Concepts in semantically in - consistent groups are more likely to contain semantic type assignment errors. Our methodology can make auditing more efficient by limiting auditing resources on concepts of semantically inconsistent groups., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Schattauer GmbH.)

The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology. NDF-RT provides important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles. It is difficult to comprehend large, complex terminologies like NDF-RT. In previous studies, we introduced abstraction networks to summarize the content and structure of terminologies. In this paper, we introduce the Ingredient Abstraction Network to summarize NDF-RT's Chemical Ingredients and their associated drugs. Additionally, we introduce the Aggregate Ingredient Abstraction Network, for controlling the granularity of summarization provided by the Ingredient Abstraction Network. The Ingredient Abstraction Network is used to support the discovery of new candidate drug-drug interactions (DDIs) not appearing in First Databank, Inc.'s DDI knowledgebase.

Abstraction networks are compact summarizations of terminologies used to support orientation and terminology quality assurance (TQA). Area taxonomies and partial-area taxonomies are abstraction networks that have been successfully employed in support of TQA of small SNOMED CT hierarchies. However, nearly half of SNOMED CT's concepts are in the large Procedure and Clinical Finding hierarchies. Abstraction network derivation methodologies applied to those hierarchies resulted in taxonomies that were too large to effectively support TQA. A methodology for deriving sub-taxonomies from large taxonomies is presented, and the resultant smaller abstraction networks are shown to facilitate TQA, allowing for the scaling of our taxonomy-based TQA regimen to large hierarchies. Specifically, sub-taxonomies are derived for the Procedure hierarchy and a review for errors and inconsistencies is performed. Concepts are divided into groups within the sub-taxonomy framework, and it is shown that small groups are statistically more likely to harbor erroneous and inconsistent concepts than large groups.

The UMLS is a terminological system that integrates many source terminologies. Each concept in the UMLS is assigned one or more semantic types from the Semantic Network, an upper level ontology for biomedicine. Due to the complexity of the UMLS, errors exist in the semantic type assignments. Finding assignment errors may unearth modeling errors. Even with sophisticated tools, discovering assignment errors requires manual review. In this paper we describe the evaluation of an auditing project of UMLS semantic type assignments. We studied the performance of the auditors who reviewed potential errors. We found that four auditors, interacting according to a multi-step protocol, identified a high rate of errors (one or more errors in 81% of concepts studied) and that results were sufficiently reliable (0.67 to 0.70) for the two most common types of errors. However, reliability was low for each individual auditor, suggesting that review of potential errors is resource-intensive.

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.

Semantic networks (SNs) are excellent knowledge representation structures. However, large semantic networks are hard to comprehend. To overcome this difficulty, several methods of partitioning have been developed that rely on different mixes of structural and semantic methods. However, little has appeared in the literature concerning the question whether a partition of a semantic network creates subnetworks that agree with human insight. We address this issue by presenting a comparison between the results of an algorithmic partitioning method and a partition created by a group of experts. Subsequently, we show how a network partition can be used to generate various partial views of a semantic network, which facilitate user orientation. Examples from the Unified Medical Language System (UMLS) SN are used to demonstrate partial views.

The Unified Medical Language System integrates about 800,000 concepts from 99 biomedical terminologies. Each concept is assigned to at least one semantic type of the Semantic Network. During the integration, it is unavoidable that some classification errors and inconsistencies will be introduced. In this paper, we present an auditing technique to find such errors and inconsistencies. Our technique is based on an expert reviewing the pure intersections of meta-semantic types of the metaschema, a compact abstract view of the Semantic Network. Results regarding the pure intersections are reported. The analysis results for pure intersections with 1 to 6 concepts are presented. Various kinds of errors are identified.