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A Terminology Server for medical language and medical information systems.

GALEN is developing a Terminology Server to support the development and integration of clinical systems through a range of key terminological services, built around a language-independent, re-usable, shared system of concepts--the CORE model. The focus is on supporting applications for medical records, clinical user interfaces and clinical information systems, but also includes systems for natural language understanding, clinical decision support, management of coding and classification schemes, and bibliographic retrieval. The Terminology Server integrates three modules: the Concept Module which implements the GRAIL formalism and manages the internal representation of concept entities, the Multilingual Module which manages the mapping of concept entities to natural language, and the Code Conversion Module which manages the mapping of concept entities to and from existing coding and classification schemes. The Terminology Server also provides external referencing to concept entities, coercion between data types, and makes its services available through a uniform applications programming interface. Taken together these services represent a new approach to the development of clinical systems and the sharing of medical knowledge.

Artificial Intelligence↗

Conceptual analysis for knowledge-base design.

Knowledge-base design requires a thorough analysis of the concepts to be represented. If the analysis is incomplete or inaccurate, the resulting knowledge base may contain arbitrary restrictions, inconsistent data, or limitations that make future extensions impossible. Conceptual analysis is even more critical for sharing knowledge bases between heterogeneous systems. Analyses that are adequate for independent systems may cause incompatibilities when the systems share data. This paper discusses the kinds of problems that require a careful conceptual analysis and the difficulties caused by an incorrect or incomplete analysis. It also shows how independently developed knowledge bases can be related to one another by reanalyzing and redefining the basic concepts and relations. Such analysis is essential for sharing knowledge between systems developed for different purposes, such as relational and object-oriented databases or expert systems and natural language processors.

Algorithms↗

Read Codes Version 3: a user led terminology.

Version 3 of the Read Codes was designed in response to a new set of requirements generated by three new terming initiatives in the UK, the Clinical Terms Project, the Professions Allied to Medicine Terms Project and the Nursing Terms Project. The challenge was to cope with the detail required by clinical specialists for maintaining a computerised record, to capture the natural language used by clinicians in their everyday work, and to support efficient analysis across medical records required to extract information from clinicians' individual patient data. The structure of Version 3 allows a directed acyclic graph to replace the traditional hierarchy; permits multiple such graphs if necessary; introduces qualifiers, embedding these in an information model to support analysis; introduces one-to-many mapping to external classifications where this is necessary; and maintains the tradition of a dynamic terminology that stresses the inclusion of natural clinical terms.

Artificial Intelligence↗

Navigating to knowledge.

One way to fulfill point-of-care knowledge needs is to present caregivers with a visual representation of the available "answers". Using such a representation, caregivers can recognize what they want, rather than have to recall what they need, and then navigate to an appropriate answer. Given selected pieces of information from a computer-based patient record, an interface can anticipate certain knowledge needs by initializing caregiver navigation in a semantic neighborhood of answers likely to be relevant to the patient at hand. These notions draw heavily on two collaborative projects--the U.S. National Library of Medicine Unified Medical Language System and the U.S. National Cancer Institute Knowledge Server. Both of these projects support navigation because they make the structure of medical knowledge explicit in a way that can be exploited by human interfaces.

Artificial Intelligence↗

Extended telemedical consultation using Arden Syntax based decision support, hypertext and WWW technique.

There is an obvious need for geographic distribution of expert knowledge among several health care units without increasing the cost of on-site expertise in locations where health care is provided. This paper describes the design of a knowledge-based decision-support system for extended consultation in clinical medicine. The system is based on Arden Syntax for Medical Logic Modules and hypertext using World Wide Web technology. It provides advice and explanations regarding the given advice. The explanations are presented in a hypertext format allowing the user to browse related information and to verify the relevance of the given advice. The system is intended to be used in a closed local network. With special precautions regarding issues of safety and patient security, the system can be used over wider areas such as in rural medicine. A prototype has been developed in the field of clinical microbiology and infectious diseases regarding infective endocarditis.

Artificial Intelligence↗

Evaluating a normalized conceptual representation produced from natural language patient discharge summaries.

The Menelas project aimed to produce a normalized conceptual representation from natural language patient discharge summaries. Because of the complex and detailed nature of conceptual representations, evaluating the quality of output of such a system is difficult. We present the method designed to measure the quality of Menelas output, and its application to the state of the French Menelas prototype as of the end of the project. We examine this method in the framework recently proposed by Friedman and Hripcsak. We also propose two conditions which enable to reduce the evaluation preparation workload.

Abstracting and Indexing↗

Automatic coding of diagnostic reports.

A method is presented for assigning classification codes to pathology reports by searching similar reports from an archive collection. The key for searching is textual similarity, which estimates the true, semantic similarity. This method does not require explicit modeling, and can be applied to any language or any application domain that uses natural language reporting. A number of simulation experiments was run to assess the accuracy of the method and to indicate the role of size of the archive and the transfer of document collections across laboratories. In at least 63% of the simulation trials, the most similar archive text offered a suitable classification on organ, origin and diagnosis. In 85 to 90% of the trials, the archive's best solution was found within the first five similar reports. The results indicate that the method is suitable for its purpose: suggesting potentially correct classifications to the reporting diagnostician.

Diagnosis, Computer-Assisted↗

Concepts, knowledge and language in healthcare information systems: follow-up 30 months later.

The paper reviews the last IMIA-WG6 recommendations, issued along two themes: (1) how to better share common results and (2) how to conduct new research and development efforts. Some of the major discrepancies in the field are reviewed. The paper intends to offer insight into two areas, first, in the issue of knowledge representation and, second, in the science of meaning, incorporating the broad intersection of computational linguistics, semantics, conceptual approach, and denotation. The Ogden-Richards meaning triangle is used as a convenient organizing principle to maximize the scope of understanding among the different schools, particularly in areas of linguistics and semantics on the one side and denotation and conceptual approach on the other. In conclusion, there is a need for clarification and for more scientific pragmatism.

Artificial Intelligence↗

The distinction between linguistic and conceptual semantics in medical terminology and its implication for NLP-based knowledge acquisition.

Natural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

Artificial Intelligence↗

Modeling concepts in medicine for medical language understanding.

Over the past two decades, the construction of models for medical concept representation and for understanding of the deep meaning of medical narrative texts have been challenging areas of medical informatics research. This review highlights how these two inter-related domains have evolved, emphasizing aspects of medical modeling as a tool for medical language understanding. A representation schema, which balances partially but accurately with complete but complex representations of domain-specific knowledge, must be developed to facilitate language understanding. Representative examples are drawn from two major independent efforts undertaken by the authors: the elaboration and the subsequent adjustment of the RECIT multilingual analyzer to include a robust medical concept model, and the recasting of a frame-based interlingua system, originally developed to map equivalent concepts between controlled clinical vocabularies, to invoke a similar concept model.

Artificial Intelligence↗

Metaphrase: an aid to the clinical conceptualization and formalization of patient problems in healthcare enterprises.

Patient descriptors, or "problems," such as "brain metastases of melanoma" are an effective way for caregivers to describe patients. But most problems, e.g., "cubital tunnel syndrome" or "ulnar nerve compression," found in problem lists in an Electronic Medical Record (EMR) are not comparable computationally--in general, a computer cannot determine whether they describe the same or a related problem, or whether the user would have preferred "ulnar nerve compression syndrome." Metaphrase is a scalable, middleware component designed to be accessed from problem-manager applications in EMR systems. In response to caregivers' informal descriptors it suggests potentially equivalent, authoritative, and more formally comparable descriptors. Metaphrase contains a clinical subset of the 1997 UMLS Metathesaurus and some 10,000 "problems" from the Mayo Clinic and Harvard Beth Israel Hospital. Word and term completion, spelling correction, and semantic navigation, all combine to ease the burden of problem conceptualization, entry and formalization.

Humans↗

Semantic definition of disorders in version 3 of the Read Codes.

The disorder chapter of Version 3 of the Read codes is a rich source of clinically derived terminology. The file structure has been designed to meet a clinical need to support both enumerated and compositional taxonomy. This requirement coupled with the maintenance of multiple classification necessitates a mechanism to identify consistent hierarchical placement and equivalence between concepts. The early work on the semantic definition of the disorder chapter, to support these requirements, is outlined and the attributes that have been found to be important are presented. We also describe different categories of completeness of definition that have been identified, and the scope of those that are likely to remain incompletely characterised.

Artificial Intelligence↗

Exploring the ontology of surgical procedures in the Read Thesaurus.

The Read Thesaurus is a comprehensive user-led clinical vocabulary developed from earlier, and structurally simpler, versions of the Read Codes, with substantial input from United Kingdom health care professionals. A constituent template table underpins a range of functions, including semantic definition of concepts using object-attribute-value triples. Concept representation for surgical procedures has been investigated by a number of groups and a standard European structure has been proposed. Over 50% of the surgical procedures in the Read Thesaurus have been fully characterised using a number of attributes each with a defined concept field. We report progress to date and, based on our large-scale experience, examine the applicability of the European model to a user-defined terminology.

Europe↗

Bayesian modeling for linking causally related observations in chest X-ray reports.

Our natural language understanding system outputs a list of diseases, findings, and appliances found in a chest x-ray report. The system described in this paper links those diseases and findings that are causally related. Using Bayesian networks to model the conceptual and diagnostic information found in a chest x-ray we are able to infer more specific information about the findings that are linked to diseases.

Algorithms↗

SAPHIRE International: a tool for cross-language information retrieval.

The world's foremost medical literature is written in English, yet much of the world does not speak English as a primary language. This has led to increasing research interest in cross-language information retrieval, where textual databases are queried in languages other than the one in which they are written. We describe enhancements to the SAPHIRE concept-retrieval system, which maps free-text documents and queries to concepts in the UMLS Metathesaurus, that allow it to accept text input and provide Metathesaurus concept output in any of six languages: English, German, French, Russian, Spanish, and Portuguese. An example of the use of SAPHIRE International is shown in the CliniWeb catalogue of clinically-oriented Web pages. A formative evaluation of German terms shows that additional work is required in handling plural and other suffix variants as well as expanding the breadth of synonyms in the UMLS Metathesaurus.

Clinical Medicine↗

Morpho-semantic parsing of medical expressions.

The task of editing, indexing, storing, and retrieving medical expressions within medical records remains the main objective for the years to come. Therefore, the need for a parser with semantic capabilities able to robustly extract an essential part of the knowledge embedded in the medical record is paramount. The minimal requirements before considering clinical trials are that such a system has to be in position to handle any source of medical information and to conveniently grasp the main key concepts with low silence, good recognition of modalities and acceptable noise. This paper shows that the potential of morpho-semantic parsing is high to meet these conditions. This technique is an important complement to the traditional lexical approach and to expression-oriented systems like controlled vocabularies.

Language↗

Review of biomedical knowledge and data representation with conceptual graphs.

The basis of conceptual graphs theory is an ontology of types of concepts. Concepts issued from the ontology are interlinked by semantic relationships and constitute canonical conceptual graphs. Canonical graphs may be combined to derive new conceptual graphs by means of formation rules. This formalism allows to separate knowledge representation into a conceptual level and a domain-dependent level, and enables to share and reuse a representation. This paper presents conceptual graph applications to biomedical data and concept representation, classification systems, information retrieval, and natural language understanding and processing. A discussion on the unifying role conceptual graphs theory plays in the implementation of knowledge-based systems is also presented.

Artificial Intelligence↗