The design and implementation of a ventilator-management advisor

Rutledge, Geoffrey W.; Thomsen, George; Farr, Brad R.; Tovar, Maria A.; Polaschek, Jeanette X.; Beinlich, Ingo A.; Sheiner, Lewis B.; Fagan, Lawrence M.

doi:10.1016/0933-3657(93)90006-o

Cited by 64 publications

(19 citation statements)

References 6 publications

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“…This system used the patient's measured respiratory rate, tidal volume, and the end-tidal pressure of carbon dioxide as inputs, and if these data remained in a predefined "comfort zone," the level of pressure support was automatically and incrementally reduced to wean the patient from the ventilator. A computerized system for ventilation of adult patients was presented later (Rutledge et al, 1993). This system used a physiological model of the patient rather than clinical rules to determine ventilator parameters.…”

Section: An Overview Of Various Idsss For Mechanical Ventilationmentioning

confidence: 99%

Computerized Decision Support Systems for Mechanical Ventilation

Tehrani¹

2011

Efficient Decision Support Systems - Practice and Challenges in Biomedical Related Domain

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Section: An Overview Of Various Idsss For Mechanical Ventilationmentioning

confidence: 99%

Computerized Decision Support Systems for Mechanical Ventilation

Tehrani¹

2011

Efficient Decision Support Systems - Practice and Challenges in Biomedical Related Domain

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“…At the opposite, recent systems are open architectures applied to intelligent patient monitoring, generally connected to the hospital network and integrating original techniques that can be used in other contexts 40,23,27 . The current AIDIAG project at Lille University Hospital (France) 4,49 , aims to collect, process, synthesize and interpret, using knowledge bases, the set of information available at bedside.…”

Section: Current State Of the Artmentioning

confidence: 99%

“…The "tactical" component uses three physiological parameters to modify the level of assistance during pressure support mode ventilation, and to maintain the patient within a zone of acceptable ventilation defined as Zone of Respiratory Comfort: 12<RR<28 cycles/min, Vt>300 ml or 250 if weight < 55 Kg, PetCO 2 <55 mmHg or 65 mmHg if COPD). Therefore, compared to general architectures such as VENTPLAN 40 or GUARDIAN 23,27 , the approach is more modest. The originality of NéoGanesh can be stressed in three points:…”

Section: A Knowledge-based System Working In Closed-loopmentioning

confidence: 99%

Knowledge-based systems for automatic ventilatory management

Dojat¹,

Brochard²

2001

Respiratory Care Clinics of North America

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To cite this version:Michel Dojat, Laurent Brochard. Knowledge-based systems for automatic ventilatory management.. Respiratory Care Clinics, 2001, 7 (3), pp.379-96, viii. IntroductionDespite their enormous potential to facilitate bedside management, the practical role of computers in critical care environments is generally restricted to the storage and the retrieval of data coming from electronic medical devices and hospital information networks. Benefits of the use of computers in health care may be extended by the design of computerized medical assistants that can efficiently discharge the clinical staff of repetitive tasks (which, in practice, often are not performed) and, importantly, help practitioners to make efficient decisions in time. In intensive care and anesthesia, the demand for computerized medical assistants is potentially considerable, in order to filter and synthesize the growing mass of clinical parameters and information available. The progressive introduction of computerized protocols has been proposed to standardize the bedside decision making process for mechanical ventilation and to reduce unnecessary variation among practitioners 32 , reinforcing the potential impact of computerized medical assistants. Designing such assistants for intelligent monitoring, diagnosis and therapy planning tasks in Intensive Care and Anesthesia is a challenging goal that requires the modeling of several levels of knowledge ranging from low level data interpretation to high level cognitive tasks, such as planning (for a review of recent research work in this field see 12,25 ). Our goal in this special issue is to show how knowledge-based computerized assistants can be used to practically improve the closed-loop control of mechanical ventilation. Control and Planning: Two Key Points for Automatic Ventilation Management 3Modern methods of mechanical ventilation partially assist the patient's ventilation by adding a variable amount of mechanical support to his/her spontaneous activity. In this context, since the needs of the patient are evolutive, it is essential to continuously control the ventilatory support, in order to avoid excessive work of breathing and effort, discomfort and dyspnea on the one hand, or excessive support, hyperinflation and dyssynchrony on the other hand. In parallel to this ideal automatic adaptation, it may be necessary to plan the long term adaptation of the therapy according to specific medical goals. For instance, it may be indicated to gradually decrease the level of assistance in order to facilitate the weaning from the ventilator or to take into account large variations of physiological needs during the patient wake-up from anesthesia or drug intoxication.Planning and control are two different tasks that have a common goal: choosing actions over time to influence a process, based on some model of that process 9 . Control is a local task to determine what to do the next instant. Planning is a strategic task to regulate the process evolution. For control and plann...

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“…It is based on the knowledge of weaning management acquired by the clinical staff of the Intensive Care Unit at the Henri Mondor Hospital. In contrast to other systems (26,48), our system deals with a voluntarily limited problem: 1) only one mode of ventilation is managed by the system, i.e., pressure support ventilation (PSV); 2) patients ventilated with the system must have spontaneous respiratory activity. These limitations allowed us to design a closed-loop system that controls the ventilator without any intervention by the clinician.…”

Section: The Medical Problemmentioning

confidence: 99%

Effective domain-dependent reuse in medical knowledge bases

Dojat

Pachet²

1995

Computers and Biomedical Research

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Published in Comput Biomed Res 28 (1995) 403-432. AbstractKnowledge reuse is now a critical issue for most developers of medical knowledge-based systems. As a rule, reuse is addressed from an ambitious knowledge engineering perspective that focuses on reusable general purpose knowledge modules, concepts and methods. However, such a general goal fails to take into account the specific aspects of medical practice. From the point of view of the knowledge engineer, whose goal is to capture the specific features and intricacies of a given domain, this approach addresses the wrong level of generality. In this paper, we adopt a more pragmatic viewpoint, introducing the less ambitious goal of "domain-dependent limited reuse", and suggesting effective means of achieving it in practice.In a knowledge representation framework combining objects and production rules, we propose three mechanisms emerging from the combination of object-oriented programming and rule-based programming. We show how these mechanisms contribute to achieve limited reuse and to introduce useful limited variations in medical expertise.

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The design and implementation of a ventilator-management advisor

Cited by 64 publications

References 6 publications

Computerized Decision Support Systems for Mechanical Ventilation

Computerized Decision Support Systems for Mechanical Ventilation

Knowledge-based systems for automatic ventilatory management

Effective domain-dependent reuse in medical knowledge bases

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