Extensions of SNOMED taxonomy abstraction networks supporting auditing and complexity analysis

The Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) has been widely used as a standard terminology in various biomedical domains. The enhancement of the quality of SNOMED contributes to the improvement of the medical systems that it supports. In previous work, the Structural Analysis of Biomedical Ontologies Center (SABOC) team has defined the partial-area taxonomy, a hierarchical abstraction network consisting of units called partial-areas. Each partial-area comprises a set of SNOMED concepts exhibiting a particular relationship structure and being distinguished by a unique root concept. In this dissertation, some extensions and applications of the taxonomy framework are considered. Some concepts appearing in multiple partial-areas have been designated as complex due to the fact that they constitute a tangled portion of a hierarchy and can be obstacles to users trying to gain an understanding of the hierarchy’s content. A methodology for partitioning the entire collection of these so-called overlapping complex concepts into singly-rooted groups was presented. A novel auditing methodology based on an enhanced abstraction network is described. In addition, the existing abstraction network relies heavily on the structure of the outgoing relationships of the concepts. But some of SNOMED hierarchies (or subhierarchies) serve only as targets of relationships, with few or no outgoing relationships of their own. This situation impedes the applicability of the abstraction network. To deal with this problem, a variation of the above abstraction network, called the converse abstraction network (CAN) is defined and derived automatically from a given SNOMED hierarchy. An auditing methodology based on the CAN is formulated. Furthermore, a preliminary study of the complementary use of the abstraction network in description logic (DL) for quality assurance purposes pertaining to SNOMED is presented. Two complexity measures, a structural complexity measure and a hierarchical complexity measure, based on the abstraction network are introduced to quantify the complexity of a SNOMED hierarchy. An extension of the two measures is also utilized specifically to track the complexity of the versions of the SNOMED hierarchies before and after a sequence of auditing processes

Structural indicators for effective quality assurance of snomed ct

The Standardized Nomenclature of Medicine -- Clinical Terms (SNOMED CT -- further abbreviated as SCT) has been endorsed as a premier clinical terminology by many national and international organizations. The US Government has chosen SCT to play a significant role in its initiative to promote Electronic Health Record (EH R) country-wide. However, there is evidence suggesting that, at the moment, SCT is not optimally modeled for its intended use by healthcare practitioners. There is a need to perform quality assurance (QA) of SCT to help expedite its use as a reference terminology for clinical purposes as planned for EH R use. The central theme of this dissertation is to define a group-based auditing methodology to effectively identify concepts of SCT that require QA. As such, similarity sets are introduced which are groups of concepts that are lexically identical except for one word. Concepts in a similarity set are expected to be modeled in a consistent way. If not, the set is considered to be inconsistent and submitted for review by an auditor. Initial studies found 38% of such sets to be inconsistent. The effectiveness of these sets is further improved through the use of three structural indicators. Using such indicators as the number of parents, relationships and role groups, up to 70% of the similarity sets and 32.6% of the concepts are found to exhibit inconsistencies. Furthermore, positional similarity sets, which are similarity sets with the same position of the differing word in the concept’s terms, are introduced to improve the likelihood of finding errors at the concept level. This strictness in the position of the differing word increases the lexical similarity between the concepts of a set thereby increasing the contrast between lexical similarities and modeling differences. This increase in contrast increases the likelihood of finding inconsistencies. The effectiveness of positional similarity sets in finding inconsistencies is further improved by using the same three structural indicators as discussed above in the generation of these sets. An analysis of 50 sample sets with differences in the number of relationships reveal 41.6% of the concepts to be inconsistent. Moreover, a study is performed to fully automate the process of suggesting attributes to enhance the modeling of SCT concepts using positional similarity sets. A technique is also used to automatically suggest the corresponding target values. An analysis of 50 sample concepts show that, of the 103 suggested attributes, 67 are manually confirmed to be correct. Finally, a study is conducted to examine the readiness of SCT problem list (PL) to support meaningful use of EHR. The results show that the concepts in PL suffer from the same issues as general SCT concepts, although to a slightly lesser extent, and do require further QA efforts. To support such efforts, structural indicators in the form of the number of parents and the number of words are shown to be effective in ferreting out potentially problematic concepts in which QA efforts should be focused. A structural indicator to find concepts with synonymy problems is also presented by finding pairs of SCT concepts that map to the same UMLS concept

Structural analysis and auditing of SNOMED hierarchies using abstraction networks

SNOMED is one of the leading healthcare terminologies being used worldwide. Due to its sheer volume and continuing expansion, it is inevitable that errors will make their way into SNOMED. Thus, quality assurance is an important part of its maintenance cycle. A structural approach is presented in this dissertation, aiming at developing automated techniques that can aid auditors in the discovery of terminology errors more effectively and efficiently. Large SNOMED hierarchies are partitioned, based primarily on their relationships patterns, into concept groups of more manageable sizes. Three related abstraction networks with respect to a SNOMED hierarchy, namely the area taxonomy, partial-area taxonomy, and disjoint partial-area taxonomy, are derived programmatically from the partitions. Altogether they afford high-level abstraction views of the underlying hierarchy, each with different granularity. The area taxonomy gives a global structural view of a SNOMED hierarchy, while the partial-area taxonomy focuses more on the semantic uniformity and hierarchical proximity of concepts. The disjoint partial-area taxonomy is devised as an enhancement of the partial-area taxonomy and is based on the partition of the entire collection of so-called overlapping concepts into singly-rooted groups. The taxonomies are exploited as the basis for a number of systematic auditing regimens, with a theme that complex concepts are more error-prone and require special attention in auditing activities. In general, group-based auditing is promoted to achieve a more efficient review within semantically uniform groups. Certain concept groups in the different taxonomies are deemed “complex” according to various criteria and thus deserve focused auditing. Examples of these include strict inheritance regions in the partial-area taxonomy and overlapping partial-areas in the disjoint partial-area taxonomy. Multiple hypotheses are formulated to characterize the error distributions and ratios with respect to different concept groups presented by the taxonomies, and thus further establish their efficacy as vehicles for auditing. The methodologies are demonstrated using SNOMED’s Specimen hierarchy as the test bed. Auditing results are reported and analyzed to assess the hypotheses. With the use of the double bootstrap and Fisher’s exact test (two-tailed), the aforementioned hypotheses are confirmed. Auditing on various complex concept groups based on the taxonomies is shown to yield a statistically significant higher proportion of errors

Using structural and semantic methodologies to enhance biomedical terminologies

Biomedical terminologies and ontologies underlie various Health Information Systems (HISs), Electronic Health Record (EHR) Systems, Health Information Exchanges (HIEs) and health administrative systems. Moreover, the proliferation of interdisciplinary research efforts in the biomedical field is fueling the need to overcome terminological barriers when integrating knowledge from different fields into a unified research project. Therefore well-developed and well-maintained terminologies are in high demand. Most of the biomedical terminologies are large and complex, which makes it impossible for human experts to manually detect and correct all errors and inconsistencies. Automated and semi-automated Quality Assurance methodologies that focus on areas that are more likely to contain errors and inconsistencies are therefore important. In this dissertation, structural and semantic methodologies are used to enhance biomedical terminologies. The dissertation work is divided into three major parts. The first part consists of structural auditing techniques for the Semantic Network of the Unified Medical Language System (UMLS), which serves as a vocabulary knowledge base for biomedical research in various applications. Research techniques are presented on how to automatically identify and prevent erroneous semantic type assignments to concepts. The Web-based adviseEditor system is introduced to help UMLS editors to make correct multiple semantic type assignments to concepts. It is made available to the National Library of Medicine for future use in maintaining the UMLS. The second part of this dissertation is on how to enhance the conceptual content of SNOMED CT by methods of semantic harmonization. By 2015, SNOMED will become the standard terminology for EH R encoding of diagnoses and problem lists. In order to enrich the semantics and coverage of SNOMED CT for clinical and research applications, the problem of semantic harmonization between SNOMED CT and six reference terminologies is approached by 1) comparing the vertical density of SNOM ED CT with the reference terminologies to find potential concepts for export and import; and 2) categorizing the relationships between structurally congruent concepts from pairs of terminologies, with SNOMED CT being one terminology in the pair. Six kinds of configurations are observed, e.g., alternative classifications, and suggested synonyms. For each configuration, a corresponding solution is presented for enhancing one or both of the terminologies. The third part applies Quality Assurance techniques based on “Abstraction Networks” to biomedical ontologies in BioPortal. The National Center for Biomedical Ontology provides B ioPortal as a repository of over 350 biomedical ontologies covering a wide range of domains. It is extremely difficult to design a new Quality Assurance methodology for each ontology in BioPortal. Fortunately, groups of ontologies in BioPortal share common structural features. Thus, they can be grouped into families based on combinations of these features. A uniform Quality Assurance methodology design for each family will achieve improved efficiency, which is critical with the limited Quality Assurance resources available to most ontology curators. In this dissertation, a family-based framework covering 186 BioPortal ontologies and accompanying Quality Assurance methods based on abstraction networks are presented to tackle this problem

Designing novel abstraction networks for ontology summarization and quality assurance

Biomedical ontologies are complex knowledge representation systems. Biomedical ontologies support interdisciplinary research, interoperability of medical systems, and Electronic Healthcare Record (EHR) encoding. Ontologies represent knowledge using concepts (entities) linked by relationships. Ontologies may contain hundreds of thousands of concepts and millions of relationships. For users, the size and complexity of ontologies make it difficult to comprehend “the big picture” of an ontology\u27s content. For ontology editors, size and complexity make it difficult to uncover errors and inconsistencies. Errors in an ontology will ultimately affect applications that utilize the ontology. In prior studies abstraction networks (AbNs) were developed to provide a compact summary of an ontology\u27s content and structure. AbNs have been shown to successfully support ontology summarization and quality assurance (QA), e.g., for SNOMED CT and NCIt. Despite the success of these previous studies, several major, unaddressed issues affect the applicability and usability of AbNs. This thesis is broken into five major parts, each addressing one issue. The first part of this dissertation addresses the scalability of AbN-based QA techniques to large SNOMED CT hierarchies. Previous studies focused on relatively small hierarchies. The QA techniques developed for these small hierarchies do not scale to large hierarchies, e.g., Procedure and Clinical finding. A new type of AbN, called a subtaxonomy, is introduced to address this problem. Subtaxonomies summarize a subset of an ontology\u27s content. Several types of subtaxonomies and subtaxonomy-based QA studies are discussed. The second part of this dissertation addresses the need for summarization and QA methods for the twelve SNOMED CT hierarchies with no lateral relationships. Previously developed SNOMED CT AbN derivation methodologies, which require lateral relationships, cannot be applied to these hierarchies. The Tribal Abstraction Network (TAN) is a new type of AbN derived using only hierarchical relationships. A TAN-based QA methodology is introduced and the results of a QA review of the Observable entity hierarchy are reported. The third part focuses on the development of generic AbN derivation methods that are applicable to groups of structurally similar ontologies, e.g., those developed in the Web Ontology Language (OWL) format. Previously, AbN derivation techniques were applicable to only a single ontology at a time. AbNs that are applicable to many OWL ontologies are introduced, a preliminary study on OWL AbN granularity is reported on, and the results of several QA studies are presented. The fourth part describes Diff Abstraction Networks, which summarize and visualize the structural differences between two ontology releases. Diff Area Taxonomy and Diff Partial-area Taxonomy derivation methodologies are introduced and Diff Partial-area taxonomies are derived for three OWL ontologies. The Diff Abstraction Network approach is compared to the traditional ontology diff approach. Lastly, tools for deriving and visualizing AbNs are described. The Biomedical Layout Utility Framework is introduced to support the automatic creation, visualization, and exploration of abstraction networks for SNOMED CT and OWL ontologies