James Geller

Auditing as Part of the Terminology Design Life Cycle

OBJECTIVE To develop and test an auditing methodology for detecting errors in medical terminologies satisfying systematic inheritance. This methodology is based on various abstraction taxonomies that provide high-level views of a terminology and highlight potentially erroneous concepts. DESIGN Our auditing methodology is based on dividing concepts of a terminology into smaller, more manageable units. First, we divide the terminologys concepts into areas according to their relationships/roles. Then each multi-rooted area is further divided into partial-areas (p-areas) that are singly-rooted. Each p-area contains a set of structurally and semantically uniform concepts. Two kinds of abstraction networks, called the area taxonomy and p-area taxonomy, are derived. These taxonomies form the basis for the auditing approach. Taxonomies tend to highlight potentially erroneous concepts in areas and p-areas. Human reviewers can focus their auditing efforts on the limited number of problematic concepts following two hypotheses on the probable concentration of errors. RESULTS A sample of the area taxonomy and p-area taxonomy for the Biological Process (BP) hierarchy of the National Cancer Institute Thesaurus (NCIT) was derived from the application of our methodology to its concepts. These views led to the detection of a number of different kinds of errors that are reported, and to confirmation of the hypotheses on error concentration in this hierarchy. CONCLUSION Our auditing methodology based on area and p-area taxonomies is an efficient tool for detecting errors in terminologies satisfying systematic inheritance of roles, and thus facilitates their maintenance. This methodology concentrates a domain experts manual review on portions of the concepts with a high likelihood of errors.

Representing the UMLS as an Object-oriented Database: Modeling Issues and Advantages

OBJECTIVE The Unified Medical Language System (UMLS) combines many well-established authoritative medical informatics terminologies in one knowledge representation system. Such a resource is very valuable to the health care community and industry. However, the UMLS is very large and complex and poses serious comprehension problems for users and maintenance personnel. The authors present a representation to support the users comprehension and navigation of the UMLS. DESIGN An object-oriented database (OODB) representation is used to represent the two major components of the UMLS-the Metathesaurus and the Semantic Network-as a unified system. The semantic types of the Semantic Network are modeled as semantic type classes. Intersection classes are defined to model concepts of multiple semantic types, which are removed from the semantic type classes. RESULTS The authors provide examples of how the intersection classes help expose omissions of concepts, highlight errors of semantic type classification, and uncover ambiguities of concepts in the UMLS. The resulting UMLS OODB schema is deeper and more refined than the Semantic Network, since intersection classes are introduced. The Metathesaurus is classified into more mutually exclusive, uniform sets of concepts. The schema improves the users comprehension and navigation of the Metathesaurus. CONCLUSIONS The UMLS OODB schema supports the users comprehension and navigation of the Metathesaurus. It also helps expose and resolve modeling problems in the UMLS.

Parallel implementation of a class reasoner

Abstract One method to overcome the notorious efficiency problems of logical reasoning algorithms in AI has been to combine a general-purpose reasoner with several special-purpose reasoners for commonly used subtasks. In this paper we are using Schuberts (Schubert et al. 1983, 1987) method of implementing a special-purpose class reasoner. We show that it is possible to replace Schuberts preorder number class tree by a preorder number list without loss of functionality. This form of the algorithm lends itself perfectly towards a parallel implementation,1 and we describe design, coding and testing of such an implementation. Our algorithm is practically independent of the size of the class list, and even with several thousand nodes learning times are under a second and retrieval times are under 500 ms.

Guest Editorial: Special Issue on Auditing of Terminologies

This special issue is the first in any journal to deal exclusively with the auditing of medical terminologies and ontologies. In the early stages of many emerging technical fields, the emphasis is primarily on creating systems that work. The emphasis eventually shifts to guaranteeing a high level of quality. In fact, two sure signs that a technical field is maturing are extensive activities regarding standards—both national and international—and quality assurance initiatives. In the field of medical terminologies, we have seen a great deal of standards activity in recent years, such as the work of the ISO/ TC215 Working Group 3 (Health Informatics – Semantic Content) and the American National Standards Institute guidelines for designing controlled terminologies (ANSI/NISO Z39.19-2005). There is also a heightened awareness of the need for terminology quality assurance, which has manifested itself in different ways. For example, at the 2007 AMIA Annual Symposium (Chicago), a session was devoted exclusively to terminology assessments (S10). One can also discern a constant stream of research papers in the medical informatics literature dealing with auditing. The breadth of this work, as measured by the number of different terminologies being considered, is growing. The reference list and Table 6 of the paper by Zhu et al. [1] in this special issue give evidence to these observations. Furthermore, many health care information systems rely on terminologies to provide semantically uniform access to knowledge expressed in different ways and following various paradigms. As a result of this reliance, an error in a terminology may propagate to errors in systems ranging from decision-support systems to pharmacy information systems that are monitoring, for example, drug allergies and drug-drug interactions. Hence, an error in a terminology may further propagate, resulting in an error in the treatment of patients. Realizing these facts, the guest editors came to the conclusion that the field of medical terminologies is maturing and that there is enough interest within the research community to warrant producing this unprecedented special issue on ‘‘Auditing of Terminologies.” The intention is to capture the current state of the art in the field of terminology auditing. In addition to announcing the Call for Papers for this special issue on the JBI Web-site and in other forums, the guest editors directly solicited prominent researchers in this area to submit papers. Fortunately, most of them agreed. The current issue features many authors with outstanding track records in the areas of medical terminologies and their quality assurance. To ensure high-quality published papers, many of the authors were asked to serve in the role of reviewer as well. Some researchers who were too busy to prepare papers themselves graciously agreed

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.

A methodology for partitioning a vocabulary hierarchy into trees.

Controlled medical vocabularies are useful in application areas such as medical information systems and decision-support systems. However, such vocabularies are large and complex, and working with them can be daunting. It is important to provide a means for orienting vocabulary designers and users to the vocabularys contents. We describe a methodology for partitioning a vocabulary based on an IS-A hierarchy into small meaningful pieces. The methodology uses our disciplined modeling framework to refine the IS-A hierarchy according to prescribed rules in a process carried out by a user in conjunction with the computer. The partitioning of the hierarchy implies a partitioning of the vocabulary. We demonstrate the methodology with respect to a complex sample of the MED, an existing medical vocabulary.

Controlled Vocabularies in OODBs: Modeling Issues and Implementation

A major problem that arises in many large application domains is the discrepancy among terminologies of different information systems. The terms used by the information systems of one organization may not agree with the terms used by another organization even when they are in the same domain. Such a situation clearly impedes communication and the sharing of information, and decreases the efficiency of doing business. Problems of this nature can be overcome using a controlled vocabulary (CV), a system of concepts that consolidates and unifies the terminologies of a domain. However, CVs are large and complex and difficult to comprehend. This paper presents a methodology for representing a semantic network-based CV as an object-oriented database (OODB). We call such a representation an Object-Oriented Vocabulary Repository (OOVR). The methodology is based on a structural analysis and partitioning of the source CV. The representation of a CV as an OOVR offers both the level of support typical of database management systems and an abstract view which promotes comprehension of the CVs structure and content. After discussing the theoretical aspects of the methodology, we apply it to the MED and InterMED, two existing CVs from the medical field. A program, called the OOVR Generator, for automatically carrying out our methodology is described. Both the MED-OOVR and the InterMED-OOVR have been created using the OOVR Generator, and each exists on top of ONTOS, a commercial OODBMS. The OOVR derived from the InterMED is presently available on the Web.

The cohesive metaschema: a higher-level abstraction of the UMLS semantic network

The 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 metasemantic 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.

An OODB part-whole model: semantics, notation and implementation

Abstract The notion of a part-whole relationship plays an important role when modeling data in many advanced application domains. It is therefore important that Object-Oriented Database (OODB) systems include support for this modeling primitive. We present a comprehensive part model for OODB systems. The models foundation is a part-whole relationship that captures a variety of real-world, part-whole semantics, partitioned into four characteristic dimensions: exclusiveness, multiplicity, dependency and inheritance. These impose constraints on any ‘part’ transactions (like ‘add-part’) to ensure that the state of the database remains consistent with the prescribed part-whole semantics. They also provide functionality like deletion dependency and several kinds of inheritance, both from the part to the whole and vice versa. The part relationship gives flexibility to an application developer who simply declares the desired semantics and then lets the OODB system automatically enforce it. We also introduce a graphical notation that can be used to express the enhanced semantics in the development of OODB part-whole schemata. Our part model has been integrated into the VODAK Model Language (VML), an OODB system, with the use of its extensible metaclass mechanism.

Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies

OBJECTIVE Standards terminologies may be large and complex, making their quality assurance challenging. Some terminology quality assurance (TQA) methodologies are based on abstraction networks (AbNs), compact terminology summaries. We have tested AbNs and the performance of related TQA methodologies on small terminology hierarchies. However, some standards terminologies, for example, SNOMED, are composed of very large hierarchies. Scaling AbN TQA techniques to such hierarchies poses a significant challenge. We present a scalable subject-based approach for AbN TQA. METHODS An innovative technique is presented for scaling TQA by creating a new kind of subject-based AbN called a subtaxonomy for large hierarchies. New hypotheses about concentrations of erroneous concepts within the AbN are introduced to guide scalable TQA. RESULTS We test the TQA methodology for a subject-based subtaxonomy for the Bleeding subhierarchy in SNOMEDs large Clinical finding hierarchy. To test the error concentration hypotheses, three domain experts reviewed a sample of 300 concepts. A consensus-based evaluation identified 87 erroneous concepts. The subtaxonomy-based TQA methodology was shown to uncover statistically significantly more erroneous concepts when compared to a control sample. DISCUSSION The scalability of TQA methodologies is a challenge for large standards systems like SNOMED. We demonstrated innovative subject-based TQA techniques by identifying groups of concepts with a higher likelihood of having errors within the subtaxonomy. Scalability is achieved by reviewing a large hierarchy by subject. CONCLUSIONS An innovative methodology for scaling the derivation of AbNs and a TQA methodology was shown to perform successfully for the largest hierarchy of SNOMED.