Biomedical subjects

A M Rassinoux

Publications and source records attributed to A M Rassinoux.

34 records · Page 2Linked to original sources

PubMed1998

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↗

PubMed1998

Versatility of a multilingual and bi-directional approach for medical language processing.

At the dawn of the 21st century, we are experiencing an exponential growth of online information that is mostly textual, and that benefits from new electronic media, such as the World Wide Web (WWW), to be broadly diffused across borders. However, there is a gap to bridge between holding information and accessing in a relevant way the deep underlying knowledge. Multilingual natural language processing (NLP), once tuned, is certainly the best solution to cope with this era of textual information. This paper focuses on the lesson learned through the joint development of an analyzer and a generator of medical language, within a multilingual context. Concrete examples, derived from the efforts under way in the European GALEN-IN-USE project, illustrate the use of these linguistic tools for the handling of surgical procedures.

Multilingualism↗

PubMed1998

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↗

PubMed1997

Compositional and enumerative designs for medical language representation.

Medical language is in essence highly compositional, allowing complex information to be expressed from more elementary pieces. Embedding the expressive power of medical language into formal systems of representation is recognized in the medical informatics community as a key step towards sharing such information among medical record, decision support, and information retrieval systems. Accordingly, such representation requires managing both the expressiveness of the formalism and its computational tractability, while coping with the level of detail expected by clinical applications. These desiderata can be supported by enumerative as well as compositional approaches, as argued in this paper. These principles have been applied in recasting a frame-based system for general medical findings developed during the 1980s. The new system captures the precise meaning of a subset of over 1500 medical terms for general internal medicine identified from the Quick Medical Reference (QMR) lexicon. In order to evaluate the adequacy of this formal structure in reflecting the deep meaning of the QMR findings, a validation process was implemented. It consists of automatically rebuilding the semantic representation of the QMR findings by analyzing them through the RECIT natural language analyzer, whose semantic components have been adjusted to this frame-based model for the understanding task.

Internal Medicine↗

PubMed1996

Knowledge sources for Natural Language Processing.

This paper aims at reviewing the problem of feeding Natural Language Processing (NLP) tools with convenient linguistic knowledge in the medical domain. A syntactic approach lacks the potential to solve a number of typical situations with ambiguities and is clearly insufficient for quality treatment of natural language. On the other hand, a conceptual approach relies on some modelling of the domain, of which the elaboration is d long-term process and where the ultimate solutions are far from being recognised and universally accepted. In-between is the beauty of the compromise. How can we significantly improve the coverage of linguistic knowledge in the years to come?

Artificial Intelligence↗

PubMed1996

Modeling principles for QMR medical findings.

Structured representation of medical information is essential for ensuring the accuracy and reliability of computerized decision support applications. Such systems require input that is error-free and clinically pertinent. This paper reviews existing medical models, particularly those exploited for natural language understanding, and highlights modeling features important to future indexing of medical texts with controlled vocabularies. A hybrid representation derived from existing frame-based and conceptual-graph-based systems is proposed to represent relevant medical terms as used by experts.

Abstracting and Indexing↗

PubMed1995

Analysis of medical texts based on a sound medical model.

Automatic understanding of natural language is a complex task due to the presence of ambiguities. In particular, semantic ambiguities which are often immediately and unconsciously solved by human beings, are raised when analyzing natural language sentences by computer. The latter has to know the implicit and contextual information in order to resolve these difficulties. Nowadays in medicine, a considerable effort is deployed to model semantic contents of the medical domain. Such a task is usually performed separately from linguistic considerations. The goal of this paper is to highlight the key issues of basing a medical language processing system on a sound semantic model. To illustrate the requirements and advantages of such a conceptual approach to the analysis process, the experiment conducted to adjust the RECIT analyzer to the GALEN model is shown.

Models, Theoretical↗

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

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↗

PubMed1995

Representing clinical narratives using conceptual graphs.

The analysis of medical narratives and the generation of natural language expressions are strongly dependent on the existence of an adequate representation language. Such a language has to be expressive enough in order to handle the complexity of human reasoning in the domain. Sowa's Conceptual Graphs (CG) are an answer, and this paper presents a multilingual implementation, using French, English and German. Current developments demonstrate the feasibility of an approach to natural Language Understanding where semantic aspects are dominant, in contrast to syntax driven methods. The basic idea is to aggregate blocks of words according to semantic compatibility rules, following a method called Proximity Processing. The CG representation is gradually built, starting from single words in a semantic lexicon, to finally give a complete representation of the sentence under the form of a single CG. The process is dependent on specific rules of the medical domain, and for this reason is largely controlled by the declarative knowledge of the medical Linguistic Knowledge Base.

Artificial Intelligence↗

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

ARTEMIS-2: an application development experiment with the HELIOS environment.

A medical application is a highly complex system that embraces many data types and a very large number of data processing functions and methods. The development of integrated software engineering environments has deeply changed the conception of applications and the profile of the application developers. In this paper, we address the problem of the development process of a specific multimedia application, called ARTEMIS, within the distributed HELIOS environment. The application is intended to manage information about hypertensive patients, in particular, retrieval and display of administrative, clinical and biological data and display and analysis of digital angiography images and medical reports. The objective is to show how the developer can use, customize and organize the services HELIOS provides. A particular focus is set on reuse strategies and integration during the development process. A scenario has been realized and illustrates the current state of the application. The discussion focuses on the advantages of such distributed environments in medical application development.

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↗

PubMed1992

Natural language processing and semantical representation of medical texts.

For medical records, the challenge for the present decade is Natural Language Processing (NLP) of texts, and the construction of an adequate Knowledge Representation. This article describes the components of an NLP system, which is currently being developed in the Geneva Hospital, and within the European Community's AIM programme. They are: a Natural Language Analyser, a Conceptual Graphs Builder, a Data Base Storage component, a Query Processor, a Natural Language Generator and, in addition, a Translator, a Diagnosis Encoding System and a Literature Indexing System. Taking advantage of a closed domain of knowledge, defined around a medical specialty, a method called proximity processing has been developed. In this situation no parser of the initial text is needed, and the system is based on semantical information of near words in sentences. The benefits are: easy implementation, portability between languages, robustness towards badly-formed sentences, and a sound representation using conceptual graphs.

Abstracting and Indexing↗

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↗