Developing techniques for enhancing comprehensibility of controlled medical terminologies

A controlled medical terminology (CMT) is a collection of concepts (or terms) that are used in the medical domain. Typically, a CMT also contains attributes of those concepts and/or relationships between those concepts. Electronic CMTs are extremely useful and important for communication between and integration of independent information systems in healthcare, because data in this area is highly fragmented. A single query in this area might involve several databases, e.g., a clinical database, a pharmacy database, a radiology database, and a lab test database. Unfortunately, the extensive sizes of CMTs, often containing tens of thousands of concepts and hundreds of thousands of relationships between pairs of those concepts, impose steep learning curves for new users of such CMTs. In this dissertation, we address the problem of helping a user to orient himself in an existing large CMT. In order to help a user comprehend a large, complex CMT, we need to provide abstract views of the CMT. However, at this time, no tools exist for providing a user with such abstract views. One reason for the lack of tools is the absence of a good theory on how to partition an overwhelming CMT into manageable pieces. In this dissertation, we try to overcome the described problem by using a threepronged approach. (1) We use the power of Object-Oriented Databases to design a schema extraction process for large, complex CMTs. The schema resulting from this process provides an excellent, compact representation of the CMT. (2) We develop a theory and a methodology for partitioning a large OODI3 schema, modeled as a graph, into small meaningful units. The methodology relies on the interaction between a human and a computer, making optimal use of the human\u27s semantic knowledge and the computer\u27s speed. Furthermore, the theory and methodology developed for the scbemalevel partitioning are also adapted to the object-level of a CMT. (3) We use purely structural similarities for partitioning CMTs, eliminating the need for a human expert in the partitioning methodology mentioned above. Two large medical terminologies are used as our test beds, the Medical Entities Dictionary (MED) and the Unified Medical Language System (UMLS), which itself contains a number of terminologies

Modeling controlled vocabularies using OODBs and multilevel area diagrams

A Controlled Vocabulary (CV) is a software system of domain knowledge that consolidates and unifies the terminology of a large application domain. With a common, centralized CV, costly and time-consuming translations can be eliminated between pairs of organizations and pairs of software systems. Unfortunately, the more knowledge we put into a CV, the harder it is to understand and maintain it. In this dissertation, a comprehensive theoretical methodology for modeling CVs using Object-Oriented Database (OODB) technology is presented. We present two methods for representing a semantic network CV as an equivalent OODB, which we call an Object-Oriented Vocabulary Repository (OOVR). The first method, based on a structural analysis and partitioning of the CV, yields an OODB with a very concise schema, referred to as the OOVR schema. Due to its compact size, the schema can be displayed on one or a few computer screens and serves as an aid for comprehending and maintaining the CV. A program called the Object-Oriented Vocabulary Repository Generator (OOVR Generator) has been built to automatically generate an OOVR for a given semantic network CV. Our second methodology results in a larger schema, which, however, serves as an important tool for browsing and navigation through a CV. The OODB schemas created by both methodologies provide important abstract views of CVs. We have also defined a new type of semantic relationships called IS-A\u27 in the context of an OOVR representation. The IS-A\u27 relationships are defined on OOVR schemas to reflect certain important IS-A relationships in the underlying CV. The two OOVR representations exhibit several interesting theoretical characteristics which are formally proven in this dissertation. To provide an environment with several abstract views of a CV, we also define a paradigm called Multilevel Area Diagrams (MLADs). A MLAD is a collection of different partitions of increasing detail and decreasing abstraction derived from a CV. Users can browse at one level and then switch to another level to continue their navigation. Examples of browsing sessions are presented to show that the MLAD paradigm provides processing capabilities beyond those of a traditional object-oriented representation of a vocabulary

The cohesive metaschema: a higher-level abstraction of the UMLS Semantic Network

AbstractThe Unified Medical Language System (UMLS) joins together a group of established medical terminologies in a unified knowledge representation framework. Two major resources of the UMLS are its Metathesaurus, containing a large number of concepts, and the Semantic Network (SN), containing semantic types and forming an abstraction of the Metathesaurus. However, the SN itself is large and complex and may still be difficult to view and comprehend. Our structural partitioning technique partitions the SN into structurally uniform sets of semantic types based on the distribution of the relationships within the SN. An enhancement of the structural partition results in cohesive, singly rooted sets of semantic types. Each such set is named after its root which represents the common nature of the group. These sets of semantic types are represented by higher-level components called meta-semantic types. A network, called a metaschema, which consists of the meta-semantic types connected by hierarchical and semantic relationships is obtained and provides an abstract view supporting orientation to the SN. The metaschema is utilized to audit the UMLS classifications. We present a set of graphical views of the SN based on the metaschema to help in user orientation to the SN. A study compares the cohesive metaschema to metaschemas derived semantically by UMLS experts

Interactive and batch creation of OODB medical vocabularies

Controlled vocabularies are becoming popular for knowledge representation and querying. They are particularly helpful in the medical field since they can unify disparate terminologies and provide information in a compact, comprehensible manner. In this thesis, we present a mechanism to create OODB controlled medical vocabularies from flat-file format. We also describe a tool by which a user can interactively create, edit and browse the vocabulary. For better understanding of the structure of the vocabulary we designed our interface as a graphical editor and browser. The user of this interface will typically be a medical expert who either wants to add new concepts to the vocabulary or create a new vocabulary from scratch. We first describe our approach for creating the vocabulary from an existing flat-file format by batch processing. We then present the software architecture and design of an interactive vocabulary creator (IVC)

Object oriented partitioning for a medical vocabulary based on semantic network

Computers have become ubiquitous and indispensable part of everyday life both for personal use and in the workplace. Medicine is one of the few domains that has not fully adopted computerization. One impediment to this emanates from a communication gap between the computer science professional and the medical professional. Besides, medical terminology is full of synonyms and medical professionals use them according to their personal preferences. This lack of common terminology has prevented sharing of knowledge and automating data processing, resulting in the healthcare information explosion. Semantic network models have been developed to represent medical concepts and to provide a common repository of medical terms. These networks are huge collections of terms and deal with the concepts individually. The semantic models, however, have not resolved management and comprehension difficulties associated with large number of terms and their complex semantics. Object-Oriented modeling, as demonstrated in this thesis, provides a mature technology to model complex concepts for a computerized medical vocabulary. Object class abstraction, that represents objects in the same context, promises to solve the comprehension and management problems. Such a vocabulary would facilitate exchange of information, data processing and building decision support systems

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

Structural auditing methodologies for controlled terminologies

Several auditing methodologies for large controlled terminologies are developed. These are applied to the Unified Medical Language System XXXX and the National Cancer Institute Thesaurus (NCIT). Structural auditing methodologies are based on the structural aspects such as IS-A hierarchy relationships groups of concepts assigned to semantic types and groups of relationships defined for concepts. Structurally uniform groups of concepts tend to be semantically uniform. Structural auditing methodologies focus on concepts with unlikely or rare configuration. These concepts have a high likelihood for errors. One of the methodologies is based on comparing hierarchical relationships between the META and SN, two major knowledge sources of the UMLS. In general, a correspondence between them is expected since the SN hierarchical relationships should abstract the META hierarchical relationships. It may indicate an error when a mismatch occurs. The UMLS SN has 135 categories called semantic types. However, in spite of its medium size, the SN has limited use for comprehension purposes because it cannot be easily represented in a pictorial form, it has many (about 7,000) relationships. Therefore, a higher-level abstraction for the SN called a metaschema, is constructed. Its nodes are meta-semantic types, each representing a connected group of semantic types of the SN. One of the auditing methodologies is based on a kind of metaschema called a cohesive metaschema. The focus is placed on concepts of intersections of meta-semantic types. As is shown, such concepts have high likelihood for errors. Another auditing methodology is based on dividing the NCIT into areas according to the roles of its concepts. Moreover, each multi-rooted area is further divided into pareas that are singly rooted. Each p-area contains a group of structurally and semantically uniform concepts. These groups, as well as two derived abstraction networks called taxonomies, help in focusing on concepts with potential errors. With genomic research being at the forefront of bioscience, this auditing methodology is applied to the Gene hierarchy as well as the Biological Process hierarchy of the NCIT, since processes are very important for gene information. The results support the hypothesis that the occurrence of errors is related to the size of p-areas. Errors are more frequent for small p-areas

Enriching and designing metaschemas for the UMLS semantic network

The disparate terminologies used by various biomedical applications or professionals make the communication between them more difficult. The Unified Medical Language System (UMLS) of the National Library of Medicine (NLM) is an attempt to integrate different medical terminologies into a unified representation framework to improve decision making and the quality of patient care as well as research in the health-care field. Metathesaurus (META) and Semantic Network (SN) are two main components of the UMLS system, where the SN provides a high-level abstract of the concepts in the META. This dissertation addresses three problems of the SN. First, the SN\u27s two-tree structure is restrictive because it does not allow a semantic type to be a specialization of several other semantic types. This restriction leads to the omission of some subsumption knowledge in the SN. Secondly, the SN is large and complex for comprehension purposes and it does not come with a pictorial representation for users. As a partial solution for this problem, several metaschemas were previously built as higher-level abstractions for the SN to help users\u27 orientation. Third, there is no efficient method to evaluate each metaschema. There is no technique to obtain a consolidated metaschema acceptable for a majority of the UMLS\u27s users. In this dissertation work the author attacked the described problems by using the following approaches. (1) The SN was expanded into the Enriched Semantic Network (ESN), a multiple subsumption structure with a directed acyclic graph (DAG) IS-A hierarchy, allowing a semantic type to have multiple parents. New viable IS-A links were added as warranted. Two methodologies were presented to identify and add new viable IS-A links. The ESN serves as an extended high-level abstract of the META. (2) The ESN\u27s semantic relationship distribution and concept configuration were studied. Rules were defined to derive the ESN\u27s semantic relationship distribution from the current SN\u27s semantic relationship distribution. A mapping function was defined to map the SN\u27s concept configuration to the ESN\u27s concept configuration, avoiding redundant classifications in the ESN\u27s concept configuration. (3) Several new metaschemas for the SN and the ESN were built and evaluated based on several different partitioning techniques. Each of these metaschema can serve as a higher-level abstraction of the SN (or the ESN)