Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Fuzzy Logic”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20Linked to original sources
PubMed2004

Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system.

This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.

Decision Making↗
PubMed1997

Mathematical models in decision analysis.

Decision analysis offers powerful techniques to understand and evaluate uncertain clinical situations better. Decision analytic models are appearing with increasing frequency in health policy planning, clinical information and decision-support computer systems, evaluations of clinical pathways, development of clinical practice or utilization review guidelines, and epidemiologic research. This article describes the structure, application, and limitations of the more popular decision analytic methods, including decision trees, Markov models, Monte Carlo simulation, survival and hazard functions, fuzzy logic, and sensitivity analysis. Understanding the nature of these methods will help readers to assess better the appropriateness of their use in published reports.

Decision Support Techniques↗
PubMed2006

Defining the expert ICU nurse.

This paper explores the concept of expertise in intensive care nursing practice from the perspective of its relationship to the current driving forces in healthcare. It discusses the potential barriers to acceptance of nursing expertise in a climate in which quantification of value and cost containment run high on agendas. It argues that nursing expertise which focuses on the provision of individualised, holistic care and which is based largely on intuitive decision-making cannot and should not be reduced to being articulated in positivist terms. The principles of abduction or fuzzy logic, derived from computer science, may be useful in assisting nurses to explain in terms, which others can comprehend, the value of nursing expertise.

Art↗
PubMed2004

Processing of chromatographic data for chemometric analysis of peptide profiles from cheese extracts: a novel approach.

Chemometric analysis of chromatograms plays a fundamental role in characterization of foods or in detection of adulteration. Data for multivariate analysis of chromatographic profiles are usually obtained by visual matching (VM) of peaks, the identities of which, as for peptide profiles from cheese extracts, are often unknown. To avoid the main disadvantages of VM, which is subjective and time-consuming, a novel approach was developed. Fuzzy logic was employed to handle in a systematic way uncertainty in the position of peptide peaks, and chromatograms were processed by a rule-based membership function. Processed data consisted of classes of retention time wherein peak heights were accumulated by using the distance from the center of the class as a weight. The novel approach (fuzzy approach, FA) was compared with VM by using a real data set and by performing multivariate descriptive statistical techniques (principal component analysis, multidimensional scaling, and nonhierarchical cluster analysis). FA provided a fast, reliable, and objective alternative to VM and could be successfully applied for chemometric analysis of chromatographic profiles whenever knowledge of the identity of peaks is lacking or unnecessary.

Cheese↗
PubMed2002

A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data.

In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

Algorithms↗
PubMed2001

Advanced monitoring and control of anaerobic wastewater treatment plants: fault detection and isolation.

In this paper, a fault detection and isolation approach using fuzzy logic is described for on-line analysis of problems occurring in anaerobic digestion processes. The measurements available on the process are preprocessed to build a vector of fault residuals indicating the magnitude of the problems. This vector is classified into a prespecified category (i.e., a class) which is a state of the system, according to discrimination fuzzy rules. Three different types of classes were defined in a hierarchical structure: sensors faults, sub-process faults and process faults. This approach was developed to handle in real time both technical and biological problems. Demonstration of the practical interest of this study was made using real life experiments and large improvement of the reliability and safety of the process was obtained, thus optimizing the overall wastewater treatment.

Algorithms↗
PubMed2005

Fuzzy-based adaptive bandwidth control for loss guarantees.

This paper presents the use of adaptive bandwidth control (ABC) for a quantitative packet loss rate guarantee to aggregate traffic in packet switched networks. ABC starts with some initial amount of bandwidth allocated to a queue and adjusts it over time based on online measurements of system states to ensure that the allocated bandwidth is just enough to attain the specified loss requirement. Consequently, no a priori detailed traffic information is required, making ABC more suitable for efficient aggregate quality of service (QoS) provisioning. We propose an ABC algorithm called augmented Fuzzy (A-Fuzzy) control, whereby fuzzy logic control is used to keep an average queue length at an appropriate target value, and the measured packet loss rate is used to augment the standard control to achieve better performance. An extensive simulation study based on both theoretical traffic models and real traffic traces under a wide range of system configurations demonstrates that the A-Fuzzy control itself is highly robust, yields high bandwidth utilization, and is indeed a viable alternative and improvement to static bandwidth allocation (SBA) and existing adaptive bandwidth allocation schemes. Additionally, we develop a simple and efficient measurement-based admission control procedure which limits the amount of input traffic in order to maintain the performance of the A-Fuzzy control at an acceptable level.

Algorithms↗
PubMed2002

Thresholding reflux episodes in impedance measurements using a neuro-fuzzy system.

Multiple intraluminal impedance measurements (MII) provides impedance signals which reflect the changes in the electrical conductivity inside the gastrointestinal tract (GIT). To determine a reflux episode from MII signals, wavelet transform is used to detect the edges of the impedance, then thresholding impedance at certain level. A predictor for the value of the threshold is constructed by training a fuzzy system using a number of features. During training process the predictor clusters the features into classes of similar feature values. The trained system is used to predict the threshold applied on the distal channel. Subsequently, a combination of wavelet transform and fuzzy logic is used to classify the episodes. In this paper we present the results of one stage of the system that predicts the threshold applied on the impedance value.

Differential Threshold↗
PubMed1994

A comprehensive computerized patient record with automated linkage to QMR.

The author has developed comprehensive computerized charting software, Dr. Welford's Chart Notes Program, that interprets each portion of a patient record for the 4,653 QMR Findings and exports the identified Findings to a file that can be imported by QMR for immediate case analysis. The QMR Link utilizes a hierarchical Vocabulary System composed of approximately 20,000 terms and a "fuzzy logic" that identifies similar and implicit concepts as well as exact matches to QMR Findings. This system thereby makes it much quicker and easier to utilize QMR in evaluating a patient's illness.

Diagnosis, Computer-Assisted↗
PubMedDate not supplied

The control of blood glucose in the critical diabetic patient: a neuro-fuzzy method.

Conventional algorithms for regulating insulin infusion rates in those critical diabetic patients submitted to parenteral glucose and insulin infusions do not allow to approach near normal blood glucose (BG) levels since traditional control systems are not fully effective in complex nonlinear systems as BG control is. Thus, we applied fuzzy logic principles and neural network techniques to modify intravenous insulin administration rates during glucose infusion. Forty critically ill, fasted diabetic subjects submitted to glucose and potassium infusion entered the study. They were randomly assigned to two treatment regimes: in group A, insulin infusion rates were adjusted, every 4 h at any step between -1.5 and +1.5 U/h, according to a neuro-fuzzy nomogram; in control group B, insulin infusion rates were modified according to a conventional algorithm. In group A, BG was lowered below 10 mmol/l faster than in group B (8.2+/-0.7 vs. 13+/-1.8 h, P<.02). Mean BG was 7.8+/-0.2 in group A and 10.6+/-0.3 mmol/l in group B (P<.00001). BG values below 4.4 mmol/l were: A=5.8% and B=10.2%. BG values lower than 2.5 mmol/l had never been observed. In conclusion, the neuro-fuzzy control system is effective in improving the BG control in critical diabetic patients without increasing either the number of BG determinations or the risk of hypoglycemia.

Adult↗
PubMed1996

Performance assessment of a fuzzy controller for atracurium-induced neuromuscular block.

The performance of a fuzzy controller for atracurium-induced neuromuscular block was assessed. Ten ASA I or II patients undergoing surgery anticipated to last at least 120 min were studied. A Datex Relaxograph was used to monitor neuromuscular block. Initially the T1 set point was set at 10% of baseline for at least 30 min (phase I). The T1 set point was then increased to 20% and then returned to 10% for two further periods of at least 30 min duration (phases II and III). The mean (SD) of the mean T1 error in 10 patients for phases I, II and III were 1.1 (1.4)%, -0.43 (1.2)% and 0.28 (0.94)%, respectively. The results show that a simple fuzzy logic controller can provide good accuracy with insensitivity to set point changes despite the considerable inter-individual variation in infusion rate required.

Adult↗
PubMed2005

New mathematical methods in pharmacokinetic modeling.

In recent years, several new methods for the mathematical modeling have gradually emerged in pharmacokinetics, and the development of pharmacokinetic models based on these methods has become one of the most rapidly growing and exciting application-oriented sub-disciplines of the mathematical modeling. The goals of our MiniReview are twofold: i) to briefly outline fundamental ideas of some new modeling methods that have not been widely utilized in pharmacokinetics as yet, i.e. the methods based on the following concepts: linear time-invariant dynamic system, artificial-neural-network, fuzzy-logic, and fractal; ii) to arouse the interest of pharmacological, toxicological, and pharmaceutical scientists in the given methods, by sketching some application examples which indicate the good performance and perspective of these methods in solving pharmacokinetic problems.

Animals↗
PubMed2001

Ethics assessment as an adjunct to risk assessment in the evaluation of developmental neurotoxicants.

The conduct of experimental studies in humans is governed by a body of principles whose main precepts have evolved over the past few decades. Three of these provide the foundations for judging the ethical adequacy of such an experiment. One addresses the question of who receives the benefits of the research and who bears its burdens (justice). A second requires that the research maximize the potential benefits to the subjects and minimize the risk of harm (beneficence). The third, the source of guidelines for informed consent, requires that subjects enter into the research voluntarily and with adequate information (respect for persons). Unlike research conducted to evaluate drugs, however, environmental exposures to potentially toxic chemicals do not survey those exposed for their consent, nor do they provide an appropriate calculus for measuring risks and benefits, which typically involve two different populations. Especially for exposure to developmental neurotoxicants, where the risk-benefit incompatibility can be so striking, another element may need to be incorporated into risk characterization: a process of ethics assessment. A scheme for doing so can be derived from the procedures of fuzzy logic, which allow rules to be formulated that are applicable to ethical principles. Such an approach incorporates some of the tenets of the precautionary principle.

Child↗
PubMed1996

New methods of searching for medical literature: comparison of two systems, implementation and user reactions in a urologic department.

Knowledge Finder is the first Medline retrieval system based on CD-ROM technology that allows natural-language queries and accounts for deviations in spelling and terminology. Retrieved citations are ranked in accordance with their possible relevance (fuzzy logic algorithms), thus resulting in speedy orientation on a topic of interest. EasyOrder is an additional software module that enables the user to place an automatic electronic order at the library of choice, via modem, to receive a copy of a full-text article by mail or facsimile. We performed a study comparing search results and times when using a Boolean versus a fuzzy system. This article describes the underlying new concepts, implementation in a urologic department and user reactions to the new possibilities.

CD-ROM↗
PubMed2006

Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals.

The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the r-r interval, etc., may help to identify the nature of disease afflicting the heart. However, human observer cannot directly monitor these subtle details and it is difficult to evaluate the cardiac health using ECG alone. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. In this study, a computer based analysis and display of the heterogeneous signals for the detection of life threatening states is demonstrated using fuzzy logic based data fusion. And to evaluate the severity of the disease a new parameter, deterioration index is proposed and results are tabulated for various cases.

Electrocardiography↗
PubMed2006

Effects of diabetic neuropathy on body sway and slip perturbation detection in older population.

Postural control is a common mechanism to compensate for unexpected displacements of the body. In the older population, a slip or fall due to a failure of postural control is a common cause of morbidity and mortality. The ability of postural control decreases with aging or neuropathy. In this study, 2 groups, diabetics and non-diabetics in the older population, were compared to determine how patterns of postural sway during quiet standing were related to the detection of perturbation. The SLIP-FALLS system was applied to the measurement of sway and detection of perturbation. In phase 1 of the development of the predictive model, neural network algorithms were applied to find determinant variables for perturbation detection. In phase 2, a fuzzy logic inference system was developed to investigate the relationship between sway and perturbation detection. Results of this study may be applied to the design of floor mats or shoe insoles for preventing fatigue in workplaces.

Aging↗
PubMed2006

FCMAC-BYY: fuzzy CMAC using Bayesian Ying-Yang learning.

As an associative memory neural network model, the cerebellar model articulation controller (CMAC) has attractive properties of fast learning and simple computation, but its rigid structure makes it difficult to approximate certain functions. This research attempts to construct a novel neural fuzzy CMAC, in which Bayesian Ying-Yang (BYY) learning is introduced to determine the optimal fuzzy sets, and a truth-value restriction inference scheme is subsequently employed to derive the truth values of the rule weights of implication rules. The BYY is motivated from the famous Chinese ancient Ying-Yang philosophy: everything in the universe can be viewed as a product of a constant conflict between opposites-Ying and Yang, a perfect status is reached when Ying and Yang achieve harmony. The proposed fuzzy CMAC (FCMAC)-BYY enjoys the following advantages. First, it has a higher generalization ability because the fuzzy rule sets are systematically optimized by BYY; second, it reduces the memory requirement of the network by a significant degree as compared to the original CMAC; and third, it provides an intuitive fuzzy logic reasoning and has clear semantic meanings. The experimental results on some benchmark datasets show that the proposed FCMAC-BYY outperforms the existing representative techniques in the research literature.

Algorithms↗
PubMed1996

Fuzzy filter for state estimation of a glucoregulatory system.

A filter based on fuzzy logic for state estimation of a glucoregulatory system is presented. A published non-linear model for the dynamics of glucose and its hormonal control including a single glucose compartment, five insulin compartments and a glucagon compartment was used for simulation. The simulated data were corrupted by an additive white noise with zero mean and a coefficient of variation (CV) of between 2 and 20% and then submitted to the state estimation procedure using a fuzzy filter (FF). The performance of the FF was compared with an extended Kalman filter (EKF) for state estimation. Both the FF and the EKF were evaluated in the following cases: (a) five state variables are measurable; three plasma variables are measurable; only plasma glucose is measurable; (b) for different measurement noise levels (CV of 2-20%); and (c) a mismatch between the glucoregulatory system and the given mathematical model (uncertain or approximate model). In contrast to the FF, in the case of approximate model of the glucose system, the EKF failed to achieve useful state estimation. Moreover, the performance of the FF was independent of the noise level. In conclusion, the FF approach is a viable alternative for state estimation in a noisy environment and with an uncertain mathematical model of the glucoregulatory system.

Blood Glucose↗