Increased risk from third-generation progestogens: fuzzy logic or fuzzy science?
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Fuzzy logic applications.
Fuzzy logic is a modeling method well suited for the control of complex and non-linear systems. This paper illustrates some of the power of fuzzy logic through a simple control example. For the analytical chemist, fuzzy logic incorporates imprecision from measurement noise as well as from linguistic process descriptions to produce operational control systems.
Journal Article↗
[Decision-support guideline in obstetrical nursing: application of fuzzy logic technique].
Fuzzy Logic has been used as an approach for knowledge representation and a technique for modeling Clinical Decision-Support Systems. In considering such technique underutilization for modeling nursing clinical decisions, this essay aims to present general notions about this technique and through it to develop a theoretical formulation of practice guideline to support decision in amniotomy cases for pregnant women in normal labor.
Decision Support Techniques↗
The use of fuzzy logic in nutrition.
Fuzzy logic is a mathematical approach to deal with systems that can not be defined precisely. Nutrient requirements fall into this category. Dietary intakes of nutrients are such that if a nutrient is given in graded amounts, with all other nutrients constant, there is no definitive border where, for example, one intake is deficient and another, slightly higher intake, adequate. Thus, fuzzy sets were developed that describe the range of intakes of a nutrient, ranging from deficiency to excess. On the basis of these fuzzy sets and the known nutrient composition of the food, an index is derived that reflects how closely the diet of an individual meets all the nutrient requirements set by the German Society of Nutrition. Because this is a computer-based system, alterations in the diet are suggested if the diet does not meet requirements. The suggested dietary alterations are usually small but nevertheless allow the diet to meet recommendations. It is important that the suggested alterations be small because the fewer the suggested changes in a diet, the greater the change a person will accept the changes. Thus nutrient intake can be described and evaluated by using fuzzy decision making. This has present applicability in nutrition education and could possibly be used as a tool in determining recommended dietary allowances.
Fuzzy Logic↗
A measurement-theoretic analysis of the fuzzy logic model of perception.
The fuzzy logic model of perception (FLMP) is analyzed from a measurement-theoretic perspective. FLMP has an impressive history of fitting factorial data, suggesting that its probabilistic form is valid. The authors raise questions about the underlying processing assumptions of FLMP. Although FLMP parameters are interpreted as fuzzy logic truth values, the authors demonstrate that for several factorial designs widely used in choice experiments, most desirable fuzzy truth value properties fail to hold under permissible rescalings, suggesting that the fuzzy logic interpretation may be unwarranted. The authors show that FLMP's choice rule is equivalent to a version of G. Rasch's (1960) item response theory model, and the nature of FLMP measurement scales is transparent when stated in this form. Statistical inference theory exists for the Rasch model and its equivalent forms. In fact, FLMP can be reparameterized as a simple 2-category logit model, thereby facilitating interpretation of its measurement scales and allowing access to commercially available software for performing statistical inference.
Attention↗
Detection of epileptiform discharges in the EEG by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages.
OBJECTIVE: A multi-stage system for automated detection of epileptiform activity in the EEG has been developed and tested on pre-recorded data from 43 patients. METHODS: The system is centred on the use of an artificial neural network, known as the self-organising feature map (SOFM), as a novel pattern classifier. The role of the SOFM is to assign a probability value to incoming candidate epileptiform discharges (on a single channel basis). The multi-stage detection system consists of three major stages: mimetic, SOFM, and fuzzy logic. Fuzzy logic is introduced in order to incorporate spatial contextual information in the detection process. Through fuzzy logic it has been possible to develop an approximate model of the spatial reasoning performed by the electroencephalographer. RESULTS: The system was trained on 35 epileptiform EEGs containing over 3000 epileptiform events and tested on a different set of eight EEGs containing 190 epileptiform events (including one normal EEG). Results show that the system has a sensitivity of 55.3% and a selectivity of 82% with a false detection rate of just over seven per hour. CONCLUSIONS: Based on these initial results the overall performance is favourable when compared with other leading systems in the literature. This encourages us to further test the system on a larger population base with the ultimate aim of introducing it into routine clinical use.
Adult↗
Control loop noise rejection using fuzzy logic.
This paper describes an application of fuzzy logic to noise rejection in a control loop. This new use of fuzzy logic solves the problem of sluggish control loop response when using a set-point range to stop constant valve chattering due to noise in the output signal being sent to a control valve. Multiple related variables and a general understanding of their inter-relationship must be available for this method to be successfully applied. An overview of the specific fuzzy logic method used for this application is presented along with guidelines for the practical application. In addition, this paper includes results from the successful implementation of fuzzy logic to a control loop on a pilot plant distillation column.
Algorithms↗
[Intraoperative control of mean arterial pressure and heart rate with alfentanyl with fuzzy logic].
OBJECTIVES: To describe a fuzzy logic controller that adjust alfentanil infusion during surgery based on changes in mean arterial pressure (MAP) and heart rate (HR). MATERIAL AND METHODS: We designed a fuzzy logic controller using if ... then ... conditions written in C language, to be executed by a 486 PC with a 66 Mhz CPU. The controller was used with eight ASA I-II patients undergoing gynecological surgery under anesthesia with propofol, alfentanil and ventilated with oxygen/air. MAP and HR were input every three minutes, after which the controller generated an infusion based on those figures. We performed a statistical study of alfentanil consumption time until extubation and time of hemodynamic stability. Relative error of MAP was calculated. RESULTS: The controller was used for a total of 373 min with the eight patients. MAP was 15% below the desired level for 2.14% (18 min) of that time and was 15% over the desired level for 5.6% (21 min) of the time. MAP held steady within the range of stability for the remaining 92.26% (334 min) of the time the controller was used. The relative error of MAP was 7.8 +/- 1.5%. Mean time until extubation was 7 min and 2 s. CONCLUSIONS: We believe the controller can be used to automate taks executed by experts. The controller was useful for stabilizing HR and MAP.
Adult↗
A new correlation-based fuzzy logic clustering algorithm for fMRI.
Fuzzy logic clustering algorithms are a new class of processing strategies for functional MRI (fMRI). In this study, the ability of such methods to detect brain activation on application of a stimulus task is demonstrated. An optimization of the selected algorithm with regard to different parameters is proposed. These parameters include (a) those defining the pre-processing procedure of the data set; (b) the definition of the distance between two time courses, considered as p-dimensional vectors, where p is the number of sequential images in the fMRI data set; and (c) the number of clusters to be considered. Based on the assumption that such a clustering algorithm should cluster the pixel time courses according to their similarity and not their proximity (in terms of distance), cross-correlation-based distances are defined. A clear mathematical description of the algorithm is proposed, and its convergence is proven when similarity measures are used instead of conventional Euclidean distance. The differences between the membership function given by the algorithm and the probability are clearly exposed. The algorithm was tested on artificial data sets, as well as on data sets from six volunteers undergoing stimulation of the primary visual cortex. The fMRI maps provided by the fuzzy logic algorithm are compared to those achieved by the well established cross-correlation technique.
Algorithms↗
[New horizons in medicine. The application of "fuzzy logic" in clinical and experimental medicine].
In medicine, the study of physiological and physiopathological problems is generally programmed by elaborating models which respond to the principals of formal logic. This gives the advantage of favouring the transformation of the formal model into a mathematical model of reference which responds to the principles of the set theories. All this is in the utopian wish to obtain as a result of each research, a net answer whether positive or negative, according to the Aristotelian principal of tertium non datur. Taking this into consideration, the A. briefly traces the principles of modal logic and, in particular, those of fuzzy logic, proposing that the latter substitute the actual definition of "logic with more truth values", with that perhaps more pertinent of "logic of conditioned possibilities". After a brief synthesis on the state of the art on the application of fuzzy logic, the A. reports an example of graphic expression of fuzzy logic by demonstrating how the basic glycemic data (expressed by the vectors magnitude) revealed in a sample of healthy individuals, constituted on the whole an unbroken continuous stream of set partials. The A. calls attention to fuzzy logic as a useful instrument to elaborate in a new way the analysis of scenario qualified to acquire the necessary information to single out the critical points which characterize the potential development of any biological phenomenon.
Clinical Medicine↗
Fuzzy logic and its applications in medicine.
Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in medicine for tasks such as the interpretation of sets of medical findings, syndrome differentiation in Eastern medicine, diagnosis of diseases in Western medicine, mixed diagnosis of integrated Western and Eastern medicine, the optimal selection of medical treatments integrating Western and Eastern medicine, and for real-time monitoring of patient data. This was verified by trials with the following systems that were developed by our group in Vietnam: a fuzzy Expert System for Syndromes Differentiation in Oriental Traditional Medicine, an Expert System for Lung Diseases using fuzzy logic, Case Based Reasoning for Medical Diagnosis using fuzzy set theory, a diagnostic system combining disease diagnosis of Western Medicine with syndrome differentiation of Oriental Traditional Medicine, a fuzzy system for classification of Western and Eastern medicaments and finally, a fuzzy system for diagnosis and treatment of integrated Western and Eastern Medicine.
Diagnosis, Differential↗
Fuzzy logic alternative for analysis in the biomedical sciences.
Fuzzy logic brings new possibilities into control, modeling, data analysis, diagnostics, decision making, and other working fields in biomedical sciences. This paper presents how fuzzy logic can be used as an alternative or supplement to statistics in biomedical analysis. It shows an adaptive neuro-fuzzy inference computing in comparison with linear and curvilinear regression. The main goal of this presentation is to involve fuzzy logic in biomedical research. Thus, we carried out a mathematical treatment of the clinical sample, semen of infertile man, with the independent variable Concentration of spermatozoa and the dependent variable Number of spermatozoa by 230 observations.
Biometry↗
Fuzzy logic guided inverse treatment planning.
A fuzzy logic technique was applied to optimize the weighting factors in the objective function of an inverse treatment planning system for intensity-modulated radiation therapy (IMRT). Based on this technique, the optimization of weighting factors is guided by the fuzzy rules while the intensity spectrum is optimized by a fast-monotonic-descent method. The resultant fuzzy logic guided inverse planning system is capable of finding the optimal combination of weighting factors for different anatomical structures involved in treatment planning. This system was tested using one simulated (but clinically relevant) case and one clinical case. The results indicate that the optimal balance between the target dose and the critical organ dose is achieved by a refined combination of weighting factors. With the help of fuzzy inference, the efficiency and effectiveness of inverse planning for IMRT are substantially improved.
Algorithms↗
[Fuzzy logic and postoperative pain].
OBJECTIVE: To apply a fuzzy logic expert control system for the treatment of postoperative pain in a clinical setting. MATERIAL AND METHODS: A system was designed consisting of a perfusion pump guided by a fuzzy logic computer interface to regulate the perfusion of alfentanil in accordance with the patient's pain response. The system was also equipped with a safety device that halted perfusion in case of desaturation, bradypnea or heart rate or blood pressure variations greater than 25%. The system was used in the first 90 minutes after surgery in a patient who underwent bilateral saphenectomy. RESULTS: All system functions worked properly, maintaining the target analgesic values (visual analog scale < or = 2) 77% of the time. CONCLUSION: It is possible to use fuzzy logic to obtain adequate treatment of postoperative pain.
Adult↗
A fuzzy logic approach to assess groundwater pollution levels below agricultural fields.
A fuzzy logic approach has been developed to assess the groundwater pollution levels below agricultural fields. The data collected for Kumluca Plain of Turkey have been utilized to develop the approach. The plain is known with its intensive agricultural activities, which imply excessive application of fertilizers. The characteristics of the soils and underlying groundwater for this plain were monitored during the years 1999 and 2000. Additionally, an extensive field survey related to the types and yields of crops, fertilizer application and irrigation water was carried out. Both the soil and groundwater have exhibited high levels of nitrogen, phosphorus and salinity with considerable spatial and temporal variations. The pollution level of groundwater at several established stations within the plain were assessed using Fuzzy Logic. Water Pollution Index (WPI) values are calculated by Fuzzy Logic utilizing the most significant groundwater pollutants in the area namely nitrite, nitrate and orthophosphate together with the groundwater vulnerability to pollution. The results of the calculated WPI and the monitoring study have yielded good agreement. WPI indicated high to moderate water pollution levels at Kumluca plain depending on factors such as agricultural age, depth to groundwater, soil characteristics and vulnerability of groundwater to pollution. Fuzzy Logic approach has shown to be a practical, simple and useful tool to assess groundwater pollution levels.
Agriculture↗
Fuzzy logic pharmacokinetic modeling: application to lithium concentration prediction.
INTRODUCTION: We hypothesized that fuzzy logic could be used for pharmacokinetic modeling. Our objectives were to develop and evaluate a model for predicting serum lithium concentrations with fuzzy logic. METHODS: Steady-state pharmacokinetic data had been previously collected in 10 elderly patients (age range, 67 to 80 years) with depression who were receiving lithium once daily. Each patient had serial serum lithium concentration determinations over one 24-hour period. The resulting 137 data sets initially consisted of five input variables (age, weight, serum creatinine, lithium dose, and time since last dose) and one output variable (serum lithium concentration; range, 0.2 to 1.24 mmol/L). RESULTS: A fuzzy rulebase was created with 87 randomly chosen data sets, and predictions of serum lithium concentration were made on the basis of the remaining 50 data sets. All of the input variables except age and weight were identified as contributing to the fuzzy logic model. The average magnitude of the error in the predictions was 0.13 mmol/L (root mean squared error) with a bias (mean of the prediction errors) of 0.03 mmol/L. CONCLUSIONS: This study indicates that the use of fuzzy logic for pharmacokinetic modeling of lithium for serum concentration predictions is feasible.
Aged↗
Fuzzy logic and nursing.
In empiricism, there are only two answers for a question: black or white. Yet, subjective meanings of human behaviours and responses toward health and illness cannot be simply explained with black and white. Gray zones are needed because they are characterized by complexity and require a contextual understanding. In this paper, we present and suggest fuzzy logic as an example of theoretical bases that help transcend the conflicts between objectivity and subjectivity, respect gray zones between black and white answers for questions, and provide a contextual understanding of complex nursing phenomenon. A historical review of fuzzy logic is followed by a definition of fuzzy logic. Then, fuzzy logic is discussed in terms of its compatibility with nursing epistemological views and philosophical thoughts. Fuzzy logic agrees with three categories of epistemological views of nursing, including correspondence, coherence and pragmatism. Fuzzy logic also agrees with four major philosophical thoughts in nursing, including postempiricism, pragmatism, feminism, and postmodernism. Based on the discussion, we propose that fuzzy logic be further explored, used and developed in research and practice in the nursing areas/situations/phenomena that are characterized by complexity, ambiguousness, and vagueness.
Empiricism↗
Improved monitoring of preterm infants by Fuzzy Logic.
Keeping the oxygenation status of newborn infants within physiologic limits is a crucial task in intensive care. For this purpose several vital parameters are supervised routinely by monitors, such as electrocardiograph, transcutaneous partial oxygen pressure monitor and pulse oximeter. Each monitor issues an alarm signal whenever an upper or lower limit of the parameter(s) measured is exceeded. However, in practice it turns out, that a considerable amount of false alarms is generated by artefacts, which are attributed mostly to movements of the infants. Eliminating these false alarms would be of benefit to the staff as well as the patients of the intensive care unit. Accordingly, an automated system based on Fuzzy Logic was developed, which is capable of distinguishing between critical situations and artefacts. The system is based on a Transputer IMS T425 in a PC, which collects the data from the monitors, plots it on a colour screen, saves it to hard disk and analyses it by Fuzzy Logic. Fuzzy algorithms were developed to generate more reliable alarms. All vital parameters of eight infants, who either moved often and/or frequently produced real alarm situations, were recorded. Synchronously the infants' movements and care procedures were video taped. The data and video were analysed off line with the help of an experienced neonatologist. His judgement was compared to the analysis of the Fuzzy Logic system. The results show that it is possible to improve the reliability of the monitored data with the aid of an evaluation strategy based on Fuzzy Logic and hence distinguish between real alarm situations and movement artefacts to the extent that an application in an intensive care unit under routine conditions becomes conceivable.
Algorithms↗