Catálogo de publicaciones - libros

We have developed a prototype system to support decision making in Breast Cancer, wherein the varied nature of expertise is modelled by multiple ontologies that provide domain-specific grounding to concepts and relationships used. While the different medical experts need to be co-present at a meeting, our system employs a distributed architecture for handling data and invoking services appropriate for the requirements of this decision-making process. This distributed system is built upon Semantic Web technology, which enables the possibility of Web-based tele-medicine.

In oncology research, Tissue Microarray (TMA) technology allows for the mass treatment of hundreds of tissue samples and rapid visualisation of molecular targets. Since this technique is relatively new, there are very few dedicated information systems and little formalised knowledge about the technique is available. We therefore intend to set up an integrated system around TMA technology that is accessible from the Internet. In particular we intend to set up a multimedia document generation system to assist with TMA design.

In the medical field, it is now established that the maintenance of unambiguous thesauri is accomplished by the building of ontologies. Our task in the ed project is to help pneumologists code acts and diagnoses with a software that represents medical knowledge by an ontology of the concerned specialty. We apply natural language processing tools to corpora to develop the resources needed to build this ontology. In this paper, our objective is to develop a methodology for the knowledge engineer to build various types of medical ontologies based on terminology extraction from texts according to the differential semantics theory. Our main research hypothesis concerns the joint use of two methods: distributional analysis and recognition of semantic relationships by lexico-syntactic patterns. The expected result is the building of an ontology of pneumology.

This paper presents a method to automatically translate a large class of terms in the biomedical domain from one language to another; it is evaluated on translations between French and English. It relies on a machine-learning technique that infers transducers from examples of bilingual word pairs; no additional resource or knowledge is needed. Then, these transducers, making the most of the high regularity of translation discovered in the examples, can be used to translate unseen French terms into English or vice versa. We report evaluations that show that this technique achieves high precision, reaching up to 85% of correct translations for both French to English and English to French tasks.

Standardized medical terminologies are often used for the registration of patient data. In several situations there is a need to align these terminologies to other terminologies. Even when the terminologies cover the same domain, this is often a non-trivial task. The task is even more complicated when the terminology does not contain much structure. In this paper we describe the initial results of a procedure for mapping a terminology with little or no structure to a structure-rich terminology. This procedure uses the knowledge of the structure-rich terminology and a method for semantic explicitation of concept descriptions. The first results shows that, when compared to approaches based on syntactic analysis only, the recall can be greatly improved without sacrificing much of the precision.

PURPOSE: We evaluate how argumentation in scientific articles can be used to propose an original index pruning strategy, which significantly reduce the size of the engine’s indexes but having a limited impact on retrieval effectiveness. METHODS: A Bayesian classifier trained on explicitly structured MEDLINE abstracts generates these argumentative categories. The categories are used to generate four different argumentative indexes. A fifth index contains the complete abstract, together with the title and the list of Medical Subject Headings (MeSH) terms. This last index is used as baseline to compare results obtained when only a specific argumentative index is retrieved. RESULTS and CONCLUSION: When titles and medical subject headings are also stored in the respective indexes, querying PURPOSE and CONCLUSION indexes can respectively achieves 78.4% and 74.3% of the baseline, while the size if the index is divided by two. It is concluded that argumentation can be a powerful index pruning strategy in complement to more traditionnal approaches.

The increasing demand on both practitioners and librarians to encode medical documents with controlled vocabularies calls for automatic tools and methods to help them perform this task efficiently. This paper presents the Benchmark evaluation of the French MeSH indexing systems carried out under the umbrella of the VUMeF consortium. The CISMeF, NOMINDEX and HONMeSHMapper systems are introduced, and evaluated on a set of 82 resources randomly taken from the CISMeF catalogue. The automatic MeSH indexing produced by each system was compared to the manual gold standard provided by the CISMeF medical librarian team. The automatic systems achieve at best a precision close to 50% at rank 1 (HONMeSHMapper, CISMeF) and HONMeSHMapper achieves the best overall F-measure. A qualitative evaluation of the indexing provided indicates that all systems tend to misevaluate the specificity of the terms to retrieve.

Ontologies are becoming increasingly important in the biomedical domain since they enable the re-use and sharing of knowledge in a formal, homogeneous and unambiguous way. In the rapidly growing field of biomedicine, knowledge is usually evolving and therefore an ontology maintenance process is required to keep the ontological knowledge up-to-date. This paper presents our approach for populating a formally defined ontology for the allergen domain exploiting PubMed abstracts on allergens and using natural language processing and machine learning techniques. This approach is composed of two stages: locating initially instances of ontology concepts in the PubMed corpus, and finding at a 2nd stage instances’ properties and relations between instances.

Time, events, changes, and processes play a major role in medical conceptual modeling. Representation of time-structures and reasoning about time-oriented medical data are important theoretical and practical research areas. We assume that a formal representation of temporal knowledge must use as a framework some top-level ontology which describes the most general categories of temporal entities. In the current paper we discuss an ontology of time and situoids which is part of the top-level ontology GFO (General Formal Ontology) being developed by the Onto-Med research group [1]. The expressive power of GFO and its usability in conceptual modeling is tested by Onto-Med by carrying out a number of case studies in several fields of medicine and biomedicine. In the present paper we report on results of reconstructing the temporal-abstraction ontology presented by Y. Shahar [2] within GFO. In carrying out this investigation it turns out that a number of aspects in [2] needs further clarification and foundation.

About 44 treatment schemes including 40 medicines prepared from Andean medicinal herbs to cure a great variety of diseases, such as cancer and other serious illnesses, have been developed. The main problem is to choose a course of medical treatment of any disease from this variety, depending on particular criteria characterizing a patient. A classification approach may be used for this purpose. Classification is a very important aspect of decision making. This means the prescription of objects to particular classes. Classified objects are described by various criteria that can be qualitatively or quantitatively evaluated. In multicriteria environment it is hardly possible to achieve this without resorting to special techniques. The paper presents a feasibility study of using verbal classification for determining a course of medical treatment, depending on a particular disease and patient’s personality.