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This article explains how techniques of artificial intelligence are applied to expert systems for acoustic-phonetic decoding, phonological interpretation, and multi-knowledge sources for man-machine dialogue implementation. The basic ideas are illustrated with short examples. (Author/JDH)

Describes methods used to increase the participation of staff and students in their own searching at the European Institute for Business Administration. Areas discussed include the use of CD-ROM and floppy disks as alternatives to online searching, and the development of a prototype expert system to help users move between online and offline searching. (eight references) (CLB)

Aged road pavements and insufficient maintenance budgets, along with increasing concerns over the environmental issues related to transportation have introduced additional challenges to highway agencies. Multiobjective optimisation techniques can be used to account for those multiple aspects in the design of maintenance and rehabilitation (M&R) strategies. Contrary to the single-objective optimisation problems where a single solution is optimal, the solution of multiobjective optimisation problems is a set of non-dominated solutions, often referred to as Pareto-optimal set. This set of optimal solutions represents the trade-off between the different and often conflicting objectives, and in many cases is comprised by a vast number of elements. This paper presents the development and application of a fuzzy logic expert system for selecting a single solution from the Pareto set obtained from the multiobjective optimisation of sustainable pavement M&R strategies. It provides decision-makers with an easy and intuitive methodology for the selection of the most preferred solution according to sustainability criteria. The proposed system is applied to a case study from France. Posteriorly, different strategies reflecting the decision-maker's preferences towards economic and environmental objectives are analysed. Conclusions and recommendations for future improvements are derived from this application. [ABSTRACT FROM AUTHOR]

Objective: For ICD-10 coding causes of death in France in 2018 and 2019, predictions by deep neural networks (DNNs) are employed in addition to fully automatic batch coding by a rule-based expert system and to interactive coding by the coding team focused on certificates with a special public health interest and those for which DNNs have a low confidence index., Methods: Supervised seq-to-seq DNNs are trained on previously coded data to ICD-10 code multiple causes and underlying causes of death. The DNNs are then used to target death certificates to be sent to the coding team and to predict multiple causes and underlying causes of death for part of the certificates. Hence, the coding campaign for 2018 and 2019 combines three modes of coding and a loop of interaction between the three., Findings: In this campaign, 62% of the certificates are automatically batch coded by the expert system, 3% by the coding team, and the remainder by DNNs. Compared to a traditional campaign that would have relied on automatic batch coding and manual coding, the present campaign reaches an accuracy of 93.4% for ICD-10 coding of the underlying cause (95.6% at the European shortlist level). Some limitations (risks of under- or overestimation) appear for certain ICD categories, with the advantage of being quantifiable., Conclusion: The combination of the three coding methods illustrates how artificial intelligence, automated and human codings are mutually enriching. Quantified limitations on some chapters of ICD codes encourage an increase in the volume of certificates sent for manual coding from 2021 onward., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Vegetation is a good indicator that can help better manage and conserve nature. It is also essential for characterizing habitats that represent an essential component of European nature conservation policy, especially within the Natura 2000 network. However, identifying plant communities is a complex operation partly because of the lack of available tools to identify them accurately. This obstacle is particularly noticeable when we talk about diverse plant communities like meadows. This study aims to develop an expert system to apply formalized classifications of Atlantic estuarine wet meadow community types in the Natura 2000 site 'Estuaire de la Loire'. The tool we created automatically assigns vegetation plots to the units of the French vegetation typology. It allows us to ensure the classification of the European habitat types (EUNIS and the Annex I of the EU Habitats Directive) with 91% accuracy. This expert system was applied to a dataset of 1898 vegetation plots from the study area. It allowed us to link 718 vegetation plots to 4 habitats of wet meadows including the habitat of community interest 1410 'Mediterranean salt meadows (Juncetalia maritimi)'. Using this approach, we have also defined the characteristic species of these habitats at a local scale. This tool enables the fast, objective and replicable identifications of wet meadows which are necessary to map or monitor the habitat type (sensu Habitat Directive). The method applied in this study can be easily adapted in other sites and for other habitat types. [ABSTRACT FROM AUTHOR]

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El artículo discute varios proyectos de sistemas expertos en el Banco de Francia. La función del banco ha sido funcionar como banco de bancos, algo que le ha dado un lugar privilegiado en la economía francesa. La reconstrucción del sistema experto incluye tres pasos. La primera es la reestructuración de la base de conocimiento. La segunda es el establecimiento de una primera modelación con sistemas de procesamiento electrónico de datos. La tercera es la incorporación de la inteligencia artificial en el ámbito del procesamiento de datos.

The monitoring of ionic dialysance in hemodialysis allows early detection of arterio-venous fistula stenosis. One limitation to the practical use of ionic dialysance is that the analysis is very time consuming on a majority of normal cases.The purpose of the study is to evaluate the utility of an expert system reproducing a human analysis and allowing continuous monitoring of the ionic dialysance by helping the physician to focus his or her expertise on the abnormal cases.The method is based on a Bayesian model that analyzes the blood flow rate, the ionic dialysance, and the venous and arterial pressures measured on the extra corporeal circuit.The clinical evaluation was performed on 90 dialysis patients at the hospital dialysis center of Saint Brieux in France with a history of at least four consecutive months of validated recording. The retrospective automated analysis was evaluated in comparison to vascular access problems identified from invasive investigation or treatment. The sensitivity of the automated analysis is 92% with a specificity of 75%.As a conclusion we suggest that this expert system could be used in a continuous vascular access monitoring procedure consisting in a weekly review of the patient population at the dialysis center. The patients with the highest risk score need a further investigation of their historical data and their medical history in order to decide whether or not to perform an invasive intervention.

The article presents abstracts of marketing. They include "Institutionalizing Innovation: Key Activities for Market Leadership," "Pan-European Retail Business," "The View From France: Lyonnaise des Eaux," "Culture and Consumer Behavior," "Developing Marketing Expert Systems," and "Universal Product Codes."

Current ante mortem inspection involves a check of relevant Food Chain Information (FCI) transmitted by the farmer to the slaughterhouse on a regulatory FCI document. Since 2000, a farm sanitary form with FCI data has been used for all consignments of broiler chickens in France. However, the FCI needs to be standardized for the collection and interpretation of data. A study was conducted to develop an expert system, undertaken to elaborate on a simple decision support system capable of predicting whether the flocks will present a high condemnation risk, based on FCI. For this, 'optimal' (i.e. on-farm survey data) and 'worthy' (i.e. farmers' declaration on existing farm sanitary form) data quality conditions were considered to estimate the lower and upper reference bounds of the confidence that the decision-makers could have in such a tool. Chicken broiler flocks (404) were randomly selected in 15 slaughterhouses located in Western France in 2005. Condemnation proportion and farm sanitary form were collected for each selected flock. Information about health history and technical performances were also specifically collected on farm. Condemnation risk category was modelled from the on-farm collected information, using a Bayesian network and assuming this represented the optimal data quality conditions. Corresponding information declared by the farmer on the existing farm sanitary form was secondly used in the network to evaluate the impact of the uncertainty of such information on the condemnation classification obtained with the expert system. The learnt Bayesian network had 16 explanatory variables pertaining to technical characteristics and sanitary features of the flock. Using a threshold of 1% of condemned carcases to define high risk, the network sensitivity and specificity were 55% and 93%, respectively, corresponding to positive and negative predictive values of 70% and 87%. When declared existing information was used in the network, the sensitivity and specificity were 16% and 96%, respectively, corresponding to positive and negative predictive values of 57% and 80%. Results suggested that the predictive network developed may be insufficient for correctly classifying chicken flocks for targeting of management procedures, and in its current form, the expert system may be unlikely to be implemented in the field. However, it could help to improve the standardization of both form design and FCI interpretation at a national level., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Unlabelled: The present work aims at discovering new associations between medical concepts to be exploited as input in retrieval and indexing., Material and Methods: Association rules method is applied to documents. The process is carried out on three major document categories referring to e-health information consumers: health professionals, students and lay people. Association rules evaluation is founded on statistical measures combined with domain knowledge., Results: Association rules represent existing relations between medical concepts (60.62%) and new knowledge (54.21%). Based on observations, 463 expert rules are defined by medical librarians for retrieval and indexing., Conclusions: Association rules bear out existing relations, produce new knowledge and support users and indexers in document retrieval and indexing.

Pervasive Telemedicine is an emerging research discipline, which focuses on the development and the application of ubiquitous computing technology for healthcare purposes. However, the current telemedicine systems lack to be self adaptable to handle different types of data such as vital biosignals, images, video and textual data. In addition, they do not use the full capabilities of the computing devices on which they run. Unfortunately, the existing telemedicine systems do not pay enough attention to the quality level of their offered services nor offer adequate resources management for meeting bandwidth and end-to-end communication delays. In this paper we propose an information and communication architecture of a generic telemedicine system based on a knowledge base and intelligent agents interacting each with the other in a synergetic way to perform several medical tasks for a high level of quality of service (QoS). The medical assistance to skiers and high mountains resorts residents will be used in particular as an example of applicability scenario and models personalization.

Introduction: CISMeF is a Quality Controlled Health Gateway using a terminology based on the Medical Subject Headings (MeSH) thesaurus that displays medical specialties (metaterms) and the relationships existing between them and MeSH terms., Objective: The need to classify the resources within the catalogue has led us to combine this type of semantic information with domain expert knowledge for health resources categorization purposes., Material and Methods: A two-step categorization process consisting of mapping resource keywords to CISMeF metaterms and ranking metaterms by decreasing coverage in the resource has been developed. We evaluate this algorithm on a random set of 123 resources extracted from the CISMeF catalogue. Our gold standard for this evaluation is the manual classification provided by a domain expert, viz. a librarian of the team., Results: The CISMeF algorithm shows 81% precision and 93% recall, and 62% of the resources were assigned a "fully relevant" or "fairly relevant" categorization according to strict standards., Discussion: A thorough analysis of the results has enabled us to find gaps in the knowledge modeling of the CISMeF terminology. The necessary adjustments having been made, the algorithm is currently used in CISMeF for resource categorization.

Question-answering (QA) systems, as have been presented and evaluated in several TREC conferences, are the next generation of search engines. They combine 'traditional' Information Retrieval (IR) with Natural Language Processing (NLP) and Knowledge Engineering techniques to provide shorter, more precise answers to natural language questions. We study here the feasibility of such a system for French in the health care domain. In this purpose, we collected a corpus of student questions in oral surgery. We examined two enabling conditions: on the IR side, how to select the right keywords in a question to identify relevant material on the Web for answering this question, a prerequisite for success; and on the NLP side, whether the contents of the questions fit the conceptual model of an existing QA prototype, a favorable condition for rapid implementation. A manual Web search enabled us to devise automatable principles for building IR queries for these questions. Besides, we could design a semantic model, using UMLS Semantic Network relations, which is consistent with our prototype and covers 90% of the questions. However, the high specialization of the domain and the clinical orientation of the questions, joined with the more limited resources online in the French language, may restrain the quantity of Web material available for answering these questions.

Existing Problem-Based Learning (PBL) problems, though suitable in their own right for teaching purposes, are limited in their potential to evolve by themselves and to create new knowledge. Presently, they are based on textbook examples of past cases and/or cases that have been transcribed by a clinician. In this paper, we present (a) a tacit healthcare knowledge representation formalism called Healthcare Scenarios, (b) the relevance of healthcare scenarios in PBL in healthcare and medicine, (c) a novel PBL-Scenario-based tacit knowledge explication strategy and (d) an online PBL Problem Composer and Presenter (PBL-Online) to facilitate the acquisition and utilisation of expert-quality tacit healthcare knowledge to enrich online PBL. We employ a confluence of healthcare knowledge management tools and Internet technologies to bring tacit healthcare knowledge-enriched PBL to a global and yet more accessible level.

Like any chronic disease, hypertension is complex to manage. Despite the availability of evidence-based clinical practice guidelines in most countries, a lot of hypertensive patients remain inadequately managed. One difficulty lies in the synchronization of a patient's own therapeutic history with the guideline strategy. We propose a formal model to represent guideline-based therapeutic strategies as bi-dimensional matrices. We built the knowledge base as a two-level decision tree to be read during an hypertextual navigation. The first level is used to identify a patient-specific clinical situation on the basis of key elements of clinical examination (complication of hypertension, associated diseases). The second level aims at dynamically refining the theoretical strategy, a priori established in the guideline for the corresponding clinical situation, by the specific therapeutic history of the patient. Finally, depending on the patient's response to the ongoing treatment, the system provides a recommendation consistent with the guideline strategy, whatever the patient's past treatments. A first evaluation of the system on simulated cases has been well accepted by general practitioners.

Unlabelled: Low back pain (LBP)-related disability involves patients with chronic outcome., Objective: To identify the factors predictive of chronic evolution of acute LBP and to develop a predictive clinical index., Patients and Methods: Prospective investigation of 2487 employed patients referred for their first consultation with acute LBP (less than eight days). Chronic evolution defined by persistence of symptoms, unchanged or worse, at seven weeks. A predictive index was developed according to a logistic regression model., Results: One hundred fifty-five patients (6.2%) were considered to have unchanged or worsened LBP at the time of final evaluation, which was carried out on average 42 +/- 15 days after the initial visit, and were thus regarded as having a chronic outcome. When comparing patients with chronic outcome and the others, there were 25 elementary characteristics for which the degree of significance of the bilateral test was less than 0.01. They were introduced into a logistic regression model. Five parameters appeared to be related to chronic outcome: characteristics of current episode (isolated acute low back pain, acute exacerbation of chronic low back pain, sciatica), two daily living activity items, duration of certificate to remain off work and taking part in a sport. They were used to develop an easily applied index providing identification, as of the initial consultation, of the risk of chronic evolution., Conclusion: The early recognition of patients with LBP with high risk of chronic outcome can be achieved with an easily applied clinical index.

The Unified Medical Language System (UMLS) project aims to provide a repository of terms, concepts and relationships from several medical classifications. This work describes the possibility to enrich automatically with meaningful links the UMLS database by using description of diseases from another knowledge base, in our case ADM (Aide au Diagnostic Medical). In spite of the constraints and the difficulties to qualify the interconcept links, the results show that it is possible to find and create new links from a french knowledge database to the UMLS one. One of the interests of this work is that the automated learning of the connections could be used with others knowledge databases like expert system databases.

Over a four-year period, the systematic O-serotyping of all Pseudomonas aeruginosa isolated in Hôpital de Rodez associated with the use of a computerized expert system, facilitated the early detection of two outbreaks of nosocomial infections with multiresistant serotype O:11 and multiresistant serotype O:12 P. aeruginosa respectively involving ten patients over 16 months and six patients over six months. Over this four-year period, serotype O:12 represented 14% of 404 clinical isolates of P. aeruginosa, and most isolates of this serotype were resistant to multiple antibiotics. Combination experiments showed that fosfomycin/amikacin together were active against 86% of O:12 isolates. Fosfomycin/amikacin might be considered as a therapeutic alternative to ceftazime/amikacin for the presumptive antipseudomonal therapy of serotype O:12 infections.

This paper presents and evaluates a modular/hybrid connectionist system for speaker identification. Modularity has emerged as a powerful technique for reducing the complexity of connectionist systems, and allowing a priori knowledge to be incorporated into their design. Text-independent speaker identification is an inherently complex task where the amount of training data is often limited. It thus provides an ideal domain to test the validity of the modular/hybrid connectionist approach. To achieve such identification, we develop, in this paper, an architecture based upon the cooperation of several connectionist modules, and a Hidden Markov Model module. When tested on a population of 102 speakers extracted from the DARPA-TIMIT database, perfect identification was obtained.

Health budget cuts in developed countries have stressed the importance of the valued physician's activity's representation. Therefore, good codification standards have been necessary. Automated translation from information given directly by the physician is our chosen approach. Through conceptual modeling of nomenclatures used in the domain of Orthopedic Surgery in France, the authors show the feasibility, the advantages and the problems encountered using this kind of modeling with Orthonav, an original knowledge base built for codification of patients' records.

In this paper, we describe a qualitative heart model that is part of a computing environment, CARDIOLAB, and whose role includes the diagnosis and Computer Assisted Instruction (CAI) in cardiology. The model is based on a "deep knowledge" approach to diagnosis. Deep knowledge representations model the inner works of complex physical systems. Explicit representations of system components, component functions, and behavior allow a principled form of reasoning that extends the classical rule-based, first-generation expert systems. One of the main advantage of model-based diagnosis resides in the possibility of providing explanations to observed facts or measured data. This feature can be incorporated into CAI programs with similar benefits.

The practice of medicine is characterized by its great variability and by many rare diseases. When the medical students work in hospital units, they must learn the general medical practice in the care of the patient. The purpose of this work was to present a French multifunction decision aid system using artificial intelligence techniques and Hypercard tools for different modules. Through an ergonomic interface, the system assists the user in the construction of medical observations, suggests diagnostic hypothesis, provides documentation and helps the user perform retrieval tasks. The knowledge comes from senior experts and from the pre-existent and large knowledge database, ADM.

The aim of SETH is to give end-users specific advice concerning treatment and monitoring of adult drug poisoning. SETH is developed with an off the shelf expert system shell (KBMS) and runs on a microcomputer. Technical choices were done according to this analysis, financial considerations and portability. Currently, the database contains 1000 French drugs from 75 different toxicological classes. The SETH expert system simulates the expert reasoning, taking into account for each toxicological class delay, signs and dose. Two phases of evaluation were performed. The experimental implementation of Seth began in April 1992 in our Poison Control Centre. Since then, 1100 cases inputted by residents were analysed by SETH. The extension of the knowledge base to child poisoning began in March 1993.