A Model for Medical Knowledge Representation Application to the Analysis of Descriptive Pathology Reports

A model for evaluating interface terminologies

OBJECTIVE

Evaluations of individual terminology systems should be driven in part by the intended usages of such systems. Clinical interface terminologies support interactions between healthcare providers and computer-based applications. They aid practitioners in converting clinical "free text" thoughts into the structured, formal data representations used internally by application programs. Interface terminologies also serve the important role of presenting existing stored, encoded data to end users in human-understandable and actionable formats. The authors present a model for evaluating functional utility of interface terminologies based on these intended uses.

DESIGN

Specific parameters defined in the manuscript comprise the metrics for the evaluation model.

MEASUREMENTS

Parameters include concept accuracy, term expressivity, degree of semantic consistency for term construction and selection, adequacy of assertional knowledge supporting concepts, degree of complexity of pre-coordinated concepts, and the "human readability" of the terminology. The fundamental metric is how well the interface terminology performs in supporting correct, complete, and efficient data encoding or review by humans.

RESULTS

Authors provide examples demonstrating performance of the proposed evaluation model in selected instances.

CONCLUSION

A formal evaluation model will permit investigators to evaluate interface terminologies using a consistent and principled approach. Terminology developers and evaluators can apply the proposed model to identify areas for improving interface terminologies.

Interface terminologies: facilitating direct entry of clinical data into electronic health record systems

Previous investigators have defined clinical interface terminology as a systematic collection of health care-related phrases (terms) that supports clinicians' entry of patient-related information into computer programs, such as clinical "note capture" and decision support tools. Interface terminologies also can facilitate display of computer-stored patient information to clinician-users. Interface terminologies "interface" between clinicians' own unfettered, colloquial conceptualizations of patient descriptors and the more structured, coded internal data elements used by specific health care application programs. The intended uses of a terminology determine its conceptual underpinnings, structure, and content. As a result, the desiderata for interface terminologies differ from desiderata for health care-related terminologies used for storage (e.g., SNOMED-CT), information retrieval (e.g., MeSH), and classification (e.g., ICD9-CM). Necessary but not sufficient attributes for an interface terminology include adequate synonym coverage, presence of relevant assertional knowledge, and a balance between pre- and post-coordination. To place interface terminologies in context, this article reviews historical goals and challenges of clinical terminology development in general and then focuses on the unique features of interface terminologies.

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In France, the prevalence of End-Stage Renal Disease (ESRD) is not precisely known. The sources of information are scattered and not coordinated. Consequently, care is ill adapted to meet the demand. The Multi-Source Information System is the basis of the Renal Epidemiology and Information Network (REIN). It is dedicated to improve and organise our medical and epidemiological knowledge of ESRD and to aid public health decision-making in this area. The proposed approach is based on the datawarehouses. This model allows a unified vision of scattered data into distinct databases, for a better management, be it particular (patient follow-up) or global (regional follow-up), with a finality of aid in decision-making. Several categories of problems were considered: the global conception of the information system, the organisation of the datawarehouse, which offers different viewpoints of the data, the integration of heterogeneous data coming from different sources, data exchange and definition of a specific ontology.

A formal framework of knowledge to support rational psychoactive drug selection

Rational psychoactive drug selection is a data and knowledge intensive task which requires true expertise from clinical, pathophysiological and pharmacotherapeutic knowledge. This paper presents a framework of knowledge, which relates concepts from several disciplines required for psychoactive drug selection in a formal way. A framework, when based on formal semantics, avoids ambiguity and gives conceptual clarity and supports precise use of terminology which is required when many domain experts (clinicians, pharmacologists and basic science researchers) are involved. A formal framework permits linking of existing classification systems. It furthermore can serve as a knowledge base for drug selection decision support systems.

Understanding of medico-technical reports

Medical practice produces much in the form of written text. The under-usage of this medical text for research is largely due to difficulties in processing the information. The objective of the Aristotle project is to build an automatic data system that is capable of producing a semantic representation of the text in a canonical form. Understanding the text requires identifying objects mentioned in the text, their properties, and the links between them. The nature of the syntactic process allows the connection, step-by-step, of two lexical units. This connection is immediately controlled by the Interpreter, which assumes the semantic process and queries a knowledge base. The syntactic-semantic Interpreter processes one sentence at a time. The Assembler module links the meaning of the different sentences and structures into the output's shape.