Fuzzy logic control of mechanical ventilation during anaesthesia

Schäublin, J; Derighetti, M.; Feigenwinter, P; Petersen-Felix, Steen; Zbinden, A. M.

doi:10.1093/bja/77.5.636

Cited by 63 publications

(33 citation statements)

References 26 publications

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“…Fuzzy logic, as the name suggests, is valuable in cases where it is difficult to apply classical logic to model a system based on the knowledge available (9,10). For example, fuzzy logic has been applied in the classification of benign and malignant nuclei in cytological images (11) and to control ventilator support during mechanical ventilation (12). Of all of the DM applications available, these three hybrid, distinct, but completely different methods (SVM, fuzzy Articles Grigull and Lechner logic, and ANN) were chosen and combined in a novel program for this study.…”

mentioning

confidence: 99%

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Grigull

Lechner²

2012

Pediatr Res

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IntroductIon: This article demonstrates the capacity of a combination of different data mining (DM) methods to support diagnosis in pediatric emergency patients. By using a novel combination of these DM procedures, a computer-based diagnosis was created. methods: a support vector machine (sVM), artificial neural networks (aNNs), fuzzy logics, and a voting algorithm were simultaneously used to allocate a patient to one of 18 diagnoses (e.g., pneumonia, appendicitis). anonymized data sets of patients who presented in the emergency department (eD) of a pediatric care clinic were chosen. For each patient, 26 identical clinical and laboratory parameters were used (e.g., blood count, c-reactive protein) to finally develop the program. results: The combination of four DM operations arrived at a correct diagnosis in 98% of the cases, retrospectively. a subgroup analysis showed that the highest diagnostic accuracy was for appendicitis (97% correct diagnoses) and idiopathic thrombocytopenic purpura or erythroblastopenia (100% correct diagnoses). During the prospective testing, 81% of the patients were correctly diagnosed by the system. dIscussIon: The combination of these DM methods was suitable for proposing a diagnosis using both laboratory and clinical parameters. We conclude that an optimized combination of different but complementary DM methods might serve to assist medical decisions in the eD.

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confidence: 99%

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Grigull

Lechner²

2012

Pediatr Res

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“…In a series of ventilated patients Schaublin and co-workers 4 tested a fuzzy logic program that monitored pO 2 and endtidal CO 2 and altered ventilatory frequency and tidal volume to keep end-tidal CO 2 at a desired level. The system was deemed to perform no less well than anaesthetists using conventional techniques under similar conditions.…”

Section: Control Of Mechanical Ventilationmentioning

confidence: 99%

Fuzzy Logic and Decision-Making in Anaesthetics

Grant

Naesh

2005

J R Soc Med

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“…It is largely recognized in knowledge acquisition community that the elicitation of the knowledge level 36 (such as for instance temporal abstractions 42 applied to the context of the management of mechanical ventilation) is essential to facilitate reuse, sharing and maintenance of knowledge-based systems 34 . Recently, the need to base automatic control on clinical experience rather than on mathematical models of the couple patient-ventilator has led to the introduction of fuzzy logic in working closed-loop 41 or open-loop 35 systems. Fuzzy logic is used to represent the subjective human notions employed in decision-making, such as "high", "low", "normal" or "too high".…”

Section: Low-level Controllersmentioning

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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Fuzzy logic control of mechanical ventilation during anaesthesia

Cited by 63 publications

References 26 publications

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Fuzzy Logic and Decision-Making in Anaesthetics

Knowledge-based systems for automatic ventilatory management

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