Validation et optimisation de l'aide inspiratoire autorégulée

L aide inspiratoire auto-régulée est un mode de ventilation original, développé initialement pour la prise en charge de l insuffisant repiratoire chronique obstructif. Ce mode de ventilation met en jeu des boucles de régulation multiparamétriques, qui contrôlent et modulent le niveau et la forme de la pression d aide administrée. Ce travail présente les travaux de la validation du mode animal dans des situations physiopathologiques fréquemment observées. Les premiers essais sur le patient et les étapes des son développement industriel sont retracés. Les résultats des travaux sur l étude de la validité des paramètres ventilatoires au cours du sevrage en aide inspiratoire apportent une contribution à l optimisation de ce mode. Dans l avenir, l implémentation de ce mode sur des ventilateurs de réanimation nécessiterait des études comparatives avec d autres modes asservis au cours de la phase initiale de l insuffisance respiratoire aigüe ou du sevrage.Auto-regulated Ispiratory Support mode (ARIS) is a multiparametric closed-loop pressure assist system. Regulation takes into account minute ventilation, respiratory rate, tidal volume and maximal airway pressure. This original servo-control regulates initial level and slope of the applied pressure, resulting in a decelerating waveform of pressure and flow. The aim of this work was to validate ARIS and conventional pressure support in a pig model of rebreathing ang lung injury. The study of variability of breathing pattern during pressure support ventilation contribute to optimize this mode. A first clinical study and different steps of industrial developement are presented. Future introduction of ARIS in clinical practice requires to conduct large randomized, controlled trial comparing ARIS versus standard pressure support or versus other closed loop system control of mechanical ventilation during the acute phase of respiratory failure as welle as during weaning from mechanical ventilation.LILLE2-BU Santé-Recherche (593502101) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF

ICU Patient State Characterisation using Machine Learning in a Time Series Framework

International audienc

Think!: A unified numerical–symbolic knowledge representation scheme and reasoning system

AbstractMore and more applications of artificial intelligence technologies are made in biomedical software and equipment. These applications are multiple: intelligent alarms, intelligent monitoring, diagnosis support,…. Several different knowledge representation schemes are already in use: decision trees, first-order logic expert systems, calculations (mathematical modeling), trained neural network simulations,…. All these techniques have their own preferred field of application, and they do not overlap. Building a complete diagnosis support tool would require the use of several of these techniques. The problem is therefore communication between these very different systems and the complexity of the composite result. This paper describes the Think! formalism: a unified symbolic-connectionist representation scheme which tries to subsume some of the precited formalisms. Being able to integrate these knowledge representation schemes in a single model enables us to use existing knowledge bases and existing knowledge extraction techniques to make them communicate and work together