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Biomedical subjects

R Baud

Publications and source records attributed to R Baud.

13 recordsLinked to original sources
PubMed1996

Integrated computerized patient records: a two-year Geneva experience.

The UNIDOC system of computer-based medical records that was developed and made operational within the DIOGENE-2 Hospital Information System (HIS), is based upon a fully standardized and distributed open systems architecture. It should also be emphasized that UNIDOC illustrates a feasible marriage of the two technologies, UNIX and MS-DOS, is in many respects successful enough to be recommended as a sound general solution to medical office integration into a HIS.

Abstracting and Indexing↗
PubMed1995

Toward a medical linguistic knowledge base.

This paper presents the design of a Medical Linguistic Knowledge Base (MLKB). This MLKB is intended to be the multilingual recipient for all the declarative knowledge about languages. It includes words, their syntax and their conceptual representation, typology of concepts of the domain, rules for semantic analysis and conceptual schemata. For that purpose, Sowa's conceptual graphs are considered as an adequate knowledge representation. The MLKB will be an enormous body of information, and the difficulty to feed it and to validate it appears immediately. Therefore, it is necessary to start an international initiative to merge efforts from different groups.

Language↗
PubMed1995

Word segmentation processing: a way to exponentially extend medical dictionaries.

One of the most critical problems of automatic natural language processing (NLP) is the size of the medical lexicons. The set of compound medical words and the continual creation of new terms renders medical lexicons exhaustive beyond question. The structure of such dictionaries usually consists of two parts: 1) the morphological and sometimes syntactical information necessary to identify, on a grapheme level, a given word in a sentence, and 2) the part often devoted to conceptual knowledge associated with the recognized word. It is only when these two prerequisites are fulfilled that an attempt to understand the meaning of a whole expression is possible. The approach developed in this paper is a pragmatic way to rapidly increase the lexico-semantic part of medical dictionaries. We developed a semi-automatic tool, as a prototype to demonstrate the feasibility of this approach. This tool is able to translate almost any diagnosis expressed in French into its equivalent in the ICD-9CM coding scheme.

Dictionaries, Medical as Topic↗
PubMed1995

LUCID: a semi-automated ICD-9 encoding system.

The natural language approach to diagnosis encoding will certainly become a widespread technique during the second half of this decade. Accessing standard codes by numbers and keywords will be more and more considered a loss of time and efficiency. We present a demonstration of a natural language based encoding system for ICD, called LUCID, which considerably alleviates the burden of coding with ICD classification and enhances the quality of the encoded list of diagnoses. This tool, delivered on a PC platform, is very convivial and provides a versatile interface to any existing application based on Microsoft Windows standards.

Abstracting and Indexing↗
PubMed1995

Current trends with natural language processing.

Natural Language Processing in the medical domain becomes more and more powerful, efficient, and ready to be used in daily practice. The needs for such tools are enormous in the medical field, due to the vast amount of written texts for medical records. In the authors' point of view, the Electronic Patient Record (EPR) is achieved neither with Information Systems of all kinds nor with commercially available word processing systems. Natural Language Processing (NLP) is one dimension of the EPR, as well as Image Processing and Decision Support Systems. Analysis of medical texts to facilitate indexing and retrieval is well known. The need for a generation tool is to produce progress notes from menu driven systems. The computer systems of tomorrow cannot miss any single dimension. Since 1988, we've been developing an NLP system; it is supported by the European program AIM (Advanced Informatics in Medicine) within the GALEN and HELIOS consortium and the CERS (Commission d'Encouragement á la Recherche Scientifique) in Switzerland. The main directions of development are: a medical language analyzer, a language generator, a query processor, and dictionary building tools to support the Medical Linguistic Knowledge Base (MLKB). The knowledge representation schema is essentially based on Sowa's conceptual graphs, and the MLKB is multilingual from its design phase; it currently incorporates the English and the French languages; it will also continue using German. The goal of this demonstration is to provide evidence of what exists today, what will be soon available, and what is planned for the long term. Complete sentences will be processed in real time, and the browsing capabilities of the MLKB will be exercised. In particular, the following features will be presented: Analysis of complete sentences with verbs and relatives, as extracted from clinical narratives, with special attention to the method of "proximity processing" as developed in our group and the rule based approach to language description to resolve the specific surface language problems as well as the language independent semantic situations. Comparison of results for English, French, and German sentences, showing the commonalities between these languages and, therefore, the re-usable features and the language specific aspects. Generation of noun phrases in English and French, showing the opportunities for translation between these two languages. Application of the analyzer to build a knowledge representation of ICD under the form of conceptual graphs and presentation of the possibilities of a natural language encoding of diagnosis. Strategies for query processing through a sample of abdominal ultrasonography reports, which have been analyzed and stored under the form of conceptual graphs. Feeding in and browsing of the Medical Linguistic Knowledge Base and other Dictionary Building Tools, using the perspective of an international initiative to converge towards a multilingual universal solution, valid for the medical domain. The demonstration platform is Microsoft Windows 4 on a PC, with Microsoft Visual Basic as the GUI and Quintus Prolog as NLP tools language. The same programs were originally developed for Unix-based workstations and are available on multiple platforms under Motif and X11. .

European Union↗
PubMed1994

The HELIOS medical software engineering environment.

The aim of the HELIOS project is to create an integrated Software Engineering Environment (SEE) to facilitate the development and maintenance of medical applications. HELIOS is made of a set of software components, communicating through a software bus called the HELIOS Unification Bus. The object oriented paradigm is used both as the basic structure for building the software components and as the methodology for modelling, storing and retrieving the entities and procedures used in an application. Development standards include UNIX as operating system and X Window/MOTIF as windowing environment. One of the target applications for the HELIOS prototype is the development of a multimedia medical workstation as a front end to a hospital information system.

Computer Systems↗
PubMed1994

The component-based architecture of the HELIOS medical software engineering environment.

The constitution of highly integrated health information networks and the growth of multimedia technologies raise new challenges for the development of medical applications. We describe in this paper the general architecture of the HELIOS medical software engineering environment devoted to the development and maintenance of multimedia distributed medical applications. HELIOS is made of a set of software components, federated by a communication channel called the HELIOS Unification Bus. The HELIOS kernel includes three main components, the Analysis-Design and Environment, the Object Information System and the Interface Manager. HELIOS services consist in a collection of toolkits providing the necessary facilities to medical application developers. They include Image Related services, a Natural Language Processor, a Decision Support System and Connection services. The project gives special attention to both object-oriented approaches and software re-usability that are considered crucial steps towards the development of more reliable, coherent and integrated applications.

Computer Communication Networks↗
PubMed1994

Natural language processing of medical texts within the HELIOS environment.

A large number of hospital applications are potentially interested in natural language processing since they currently heavily depend on an efficient use of a huge amount of textual information. The need for systems that are able to accept multiple European languages is of paramount interest, as language barriers can be a strong impediment for large-scale communication in Europe, in particular regarding telemedicine. In the context of the AIM project HELIOS, the Natural Language Processing (NLP) component offers a large variety of medical services according to natural language free input. It allows the multilingual analysis of medical texts (currently in English, French and German) and the storage of the meaning of these texts under a deep knowledge representation that can be queried whenever it is needed. In addition, it provides facilities to handle knowledge source embedded into the conceptual typologies and into the dictionaries. This article aims at describing all these functionalities and their integration into the environment of the HELIOS project.

Artificial Intelligence↗
PubMed1993

Modelling for natural language understanding.

Natural Language Understanding (NLU) is a rapidly growing field in medical informatics. Its potential for tomorrow's applications is important. However, it is limited by its ability to ground its components on a solid model of the domain. This opens the way for the emergence of the discipline of medical domain modelling, as part of the vast field of Knowledge Base (KB) engineering. This article aims at describing the current development of a multilingual natural language system, strongly oriented towards the semantics of the domain. Special emphasis is presently given to the task of building a domain model, and to establish direct links with the language platform. The result is a model-driven NLU system. Numerous benefits are expected in the long term.

Artificial Intelligence↗
PubMedDate not supplied

Medical dictionaries for patient encoding systems: a methodology.

Medical language is highly compositional and makes extensive use of common roots, especially Latino-Greek roots. Besides words devoted to common sense, medical language presents some typical characteristics, especially on morphological and semantic aspects of word formation. Morphological decomposition and identification precedes semantic analysis. It is only when these two prerequisites are fulfilled that an attempt to grasp the meaning of a whole expression is made possible. The main aim of the proposed approach is that of coping with 'the lack of coverage of the medical lexical knowledge', in order to help physicians find the correct international classification for diseases (ICD) codes for a written diagnosis. The proposed methodology allows the development of a powerful dynamic dictionary dedicated to natural language processing in the field of diagnoses and narrative procedures. It describes the design of an analyser that can profit from a dictionary. The methods used have proved to be efficient for various classifications, s well as for multiple languages, as the system presently supports French, German, English and Dutch for ICD-9 and ICD-10 classifications.

Classification↗
PubMedDate not supplied

Present and future trends with NLP.

This paper reflects some desiderata on the role of Natural Language Processing (NLP) in the coming years as foreseen in the medical domain. During the Medical Informatics Europe (MIE) conference in 1997, the NLP track was composed by numerous papers on natural language, knowledge representation, nomenclatures and classifications. Indeed, the medical community is looking for solutions for the future which are going to emerge from more powerful desktop and for which the successful softwares of tomorrow are not yet identified. Presently this same community's needs remains unsatisfied with healthcare professionals writing at best their patient medical records in a text processing system. We stand today closer to the typewriter than to any modern solution which could radically transform the treatment of information related to patients. What kind of hurdles are there still in front of us before we reach this new territory?

Europe↗