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At least 991 records · Page 55Linked to original sources
PubMed2003

Improving self-reports of drug-use: numeric estimates as fuzzy sets.

AIMS: This study compared traditional crisp set to fuzzy set representations of quantitative estimates of drug use to investigate what survey respondents recall about their drug use. DESIGN: Sample survey comparing (a) responses to traditional survey questions asking for single numeric estimates to (b) responses to fuzzy set questions asking for boundary estimates of drug use. SETTING: Research facilities of a public university in a large southern California city. PARTICIPANTS: Community residents using tobacco, alcohol, marijuana or amphetamine (n = 88). MEASUREMENTS: Personal interviews in which participants reported onset, frequency and daily rate of tobacco, alcohol, marijuana and amphetamine use based on traditional survey questions and fuzzy set questions. FINDINGS: With the exception of marijuana use, fuzzy boundary estimates differed significantly from the traditional point estimates (P < 0.05). Median retest reliabilities for fuzzy boundary estimates were 0.96 (tobacco), 0.81 (alcohol), 0.96 (marijuana) and 0.95 (amphetamine). The degree of vagueness varied by behavior and drug, with estimates of daily rates showing greatest fuzziness and onset of use showing least fuzziness. CONCLUSIONS: Participants recall more about past drug use than a single numeric estimate. All numeric estimates of drug use showed fuzzy set properties. Compared to traditional self-reports of drug use, fuzzy set representations provide a more complete and detailed description of what participants recall about past drug use.

Adolescent↗
PubMed1998

Fuzzy thinking.

Philosophical analysis is increasingly applied to nursing issues. Whereas pre-analytical discussions might be sloppy, analysis should bring rigour. This paper explores some mistakes and misconceptions concerning nurse-related reasonings. It uses, as a running theme, a recent paper in the Journal of Advanced Nursing by Rolfe. The mistakes and misleadings to be found in the paper are not uncommon; so the paper's correction quest aims more widely than single author critique. The mistakes and misconceptions could initially appear abstract and without practical outcome; in order to avoid the threatened appearance, a nursing case study is presented, displaying some undesirable consequences for a nurse who is misled by her reading. It is hoped that this paper shows how some accuracy may be well leading.

Bias↗
PubMed1999

[Knowledge-based diagnosis and therapeutic recommendations with fuzzy-set theory methods in patients with acute lung failure (ARDS)].

OBJECTIVE: Since the treatment of patients with severe ARDS using the extracorporal lung assist (ECLA) methods remains a cost intensive and speculative procedure, a knowledge based computer system should be created and evaluated in order to support clinical decisions. METHODS: The model was based on the fuzzy set theory and therefore able to give decisions between yes and no, that means that a criterion could also be fulfilled to 35% or 80% for example. The development of this computer program consists of two steps: first, the entry criteria for the ECLA therapy were established within a framework of an international evaluation of clinical data from 3 centres (Berlin, Marburg, Vienna). Here, inherent vagueness, uncertainty of the occurrence and limited availability of medical data are to be considered to establish a useful tool. Secondly, this was done by grouping and weighting of parameters by the system and the status of each patient or patient group was assigned by the percentage of fulfillment of the criterion. RESULTS: By using a mixed sample of patients from these three centres, the fulfillment of entry criteria according either to definitions of Berlin or to definition of Marburg was different (68% versus 36%). Other differences (36% vs. 22% and 68% vs. 60%) were found between the fuzzy based score and the crisp score which represents the usually performed method. CONCLUSIONS: This now preevaluated minimal data set to describe severe ARDS patients based on the fuzzy set theory may be useful to evaluate patients for ECLA therapy or for another controlled ARDS-therapy.

Acute Disease↗
PubMed2003

Matching protein structures with fuzzy alignments.

Unraveling functional and ancestral relationships between proteins as well as structure-prediction procedures require powerful protein-alignment methods. A structure-alignment method is presented where the problem is mapped onto a cost function containing both fuzzy (Potts) assignment variables and atomic coordinates. The cost function is minimized by using an iterative scheme, where at each step mean field theory methods at finite "temperatures" are used for determining fuzzy assignment variables followed by exact translation and rotation of atomic coordinates weighted by their corresponding fuzzy assignment variables. The approach performs very well when compared with other methods, requires modest central processing unit consumption, and is robust with respect to choice of iteration parameters for a wide range of proteins.

Algorithms↗
PubMed1999

Time-resolved analysis and visualization of dynamic processes in living cells.

Recent development of in vivo microscopy techniques, including green fluorescent proteins, has allowed the visualization of a wide range of dynamic processes in living cells. For quantitative and visual interpretation of such processes, new concepts for time-resolved image analysis and continuous time-space visualization are required. Here, we describe a versatile and fully automated approach consisting of four techniques, namely highly sensitive object detection, fuzzy logic-based dynamic object tracking, computer graphical visualization, and measurement in time-space. Systematic model simulations were performed to evaluate the reliability of the automated object detection and tracking method. To demonstrate potential applications, the method was applied to the analysis of secretory membrane traffic and the functional dynamics of nuclear compartments enriched in pre-mRNA splicing factors.

Animals↗
PubMed2003

A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting.

The main objective of this study was to develop a hybrid neuro-fuzzy system for estimating the magnitude of EMG responses of 10 trunk muscles based on two lifting task variables (trunk velocity and trunk moment) as model inputs. The input and output variables were represented using the fuzzy membership functions. The initial fuzzy rules were generated by the neural network using true EMG data. Two different laboratory-derived EMG data sets were used for model development and validation, respectively. The mean absolute error (MAE) between the actual and model-estimated normalized EMG values was calculated. Across all muscles, the average value of MAE was 8.43% (SD=2.87%) of the normalized EMG data. The larger absolute errors occurred in the left side of the trunk, which exhibited higher levels of muscular activity. Overall, the developed model was capable of estimating the normalized EMG values with average value of the mean absolute differences of 6.4%. It was hypothesized that model performance could be improved by increasing the number of inputs, including additional task variables as well as the subjects' characteristics.

Biomechanical Phenomena↗
PubMed1997

A fuzzy linguistic model for the prediction of carpal tunnel syndrome risks in an occupational environment.

This research presents the development and evaluation of a fuzzy linguistic model designated to predict the risk of carpal tunnel syndrome (CTS) in an occupational setting. CTS has become one of the largest problems facing ergonomists and the medical community because it is developing in epidemic proportions within the occupational environment. In addition, practitioners are interested in identifying accurate methods for evaluating the risk of CTS in an occupational setting. It is hypothesized that many factors impact an individual's likelihood of developing CTS and the eventual development of CTS. This disparity in the occurrence of CTS for workers with similar backgrounds and work activities has confused researchers and has been a stumbling block in the development of a model for widespread use in evaluating the development of CTS. Thus this research is an attempt to develop a method that can be used to predict the likelihood of CTS risk in a variety of environments. The intent is that this model will be applied eventually in an occupational setting, thus model development was focused on a method that provided a usable interface and the desired system inputs can also be obtained without the benefit of a medical practitioner. The methodology involves knowledge acquisition to identify and categorize a holistic set of risk factors that include task-related, personal, and organizational categories. The determination of relative factor importance was accomplished using analytic hierarchy processing (AHP) analysis. Finally a mathematical representation of the CTS risk was accomplished by utilizing fuzzy set theory in order to quantify linguistic input parameters. An evaluation of the model including determination of sensitivity and specificity is conducted and the results of the model indicate that the results are fairly accurate and this method has the potential for widespread use. A significant aspect of this research is the comparison of this technique to other methods for assessing presence of CTS. The results of this evaluation technique are compared with more traditional methods for assessing the presence of CTS.

Adult↗
PubMed1998

What is 'heavy'?

One of the work practices frequently taught to employees is to estimate the heaviness of load before it is actually handled. If it is 'heavy', then one should ask for help. However, limited information can be found in the ergonomics literature about what a person perceives as a 'heavy load'. This study was conducted on 20 male and 20 female workers in the package delivery industry to estimate the amounts of load that correspond to various levels of load heaviness (e.g. 'somewhat heavy'). Experienced employees were used for this purpose. The distribution of loads within each heaviness level was developed using fuzzy sets theory. The maximum load (i.e. 23 kg) defined by the US National Institute for Occupational Safety and Health represents a 'somewhat heavy' load based on the analysis of load distribution (corresponding to a 1.0 certainty factor). Also, the 40 kg considered in the 1981 NIOSH guidelines may be classified as a 'very heavy' load. A comparative analysis of the results of this study with norms established in prior research indicates that one should be more careful in the interpretation of statistical norms for human perception of load handling. A 'moderate' level of load heaviness (i.e. 14 kg) can be handled by 85% of the worker population.

Adolescent↗
PubMed1994

Assessment of a computerized system for the diagnosis of iron deficiency.

The aim of this study was to develop a computer expert system that could reproduce a pathologist's diagnosis of iron deficiency from the data obtained from blood tests. 275 cases were collected for construction and testing of the expert system. The expert system used a combination of fuzzy set logic and cut-off points from 14 parameters to arrive at one of 5 diagnostic categories graded from "iron deficient" to "no evidence of iron deficiency". The agreement between pathologist and expert system was 0.91 (Spearman rank correlation coefficient) in the learning population; this dropped to 0.79 in the test population. Absolute agreement on diagnostic category was reached in 71% of cases. In no case was there disagreement by more than 3 grades.

Anemia, Hypochromic↗
PubMedDate not supplied

Morse code recognition system with fuzzy algorithm for disabled persons.

It is generally known that Morse code is an efficient input method for one or two switches and it is made from long and short sounds separated by silence between the sounds. The long-to-short ratio in the definition is always 3 to 1, but the long-to-short ratio variation for a disabled person is so large that it is difficult to recognize. In the last few years, several Morse code recognition methods have been successfully built on the LMS adaptive algorithms and neural network algorithm. But LMS-related adaptive algorithms need mass computation to infer the characteristic of the controller; also the neural network must learn first, by inputting some data before it is used to recognize the Morse code sequence. In this study, two fuzzy algorithms are used to recognize the unstable Morse code sequences and the result demonstrates a significant improvement of recognition for real time signal processing in a single-chip microprocessor.

Adolescent↗
PubMedDate not supplied

Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic.

This study is aimed at establishing a neural network and fuzzy feedback control FES system used for adjusting the optimum electrical stimulating current to control the motion of an ankle joint. The proposed method further improves the drop-foot problem existing in hemiplegia patients. The proposed system includes both hardware and software. The hardware system determines the patient's ankle joint angle using a position sensor located in the patient's affected side. This sensor stimulates the tibialis anterior with an electrical stimulator that induces the dorsiflexion action and achieves the ideal ankle joint trace motion. The software system estimates the stimulating current using a neural network. The fuzzy controller solves the nonlinear problem by compensating the motion trace errors between the neural network control and actual system. The control qualities of various controllers for four subjects were compared in the clinical test. It was found that both the root mean square error and the mean error were minimal when using the neural network and fuzzy controller. The drop-foot problem in hemiplegic's locomotion was effectively improved by incorporating the neural network and fuzzy controller with the functional electrical simulator.

Algorithms↗
PubMedDate not supplied

A support vector machine using the lazy learning approach for multi-class classification.

Support vector machines can be used in a new machine learning technique based on statistical learning. In this paper, we develop least squares support vector machines (LS-SVMs) using the lazy learning approach to classify data in unclassifiable regions in the case of multi-class classification. LS-SVMs use a set of linear equations while SVMs use a quadratic programming problem. The lazy learning approach is a local and memory-based technique. Therefore, it is an alternative technique to fuzzy inference systems. Our studies show that LS-SVMs with the lazy learning approach can give comparable results to fuzzy LS-SVMs for multi-class classification.

Algorithms↗
PubMed2005

Artificial neural networks: comparison of two programs for modeling a process of nanoparticle preparation.

Artificial Neural Networks (ANNs) were used to predict nanoparticle size and micropore surface area of polylactic acid nanoparticles, prepared by a double emulsion method. Different batches were prepared while varying polymer and surfactant concentration, as well as homogenization pressure. Two commercial ANNs programs were evaluated: Neuroshell Predictor, a black-box software adopting both neural and genetic strategies, and Neurosolutions, allowing a step-by-step building of the network. Results were compared to those obtained by statistical method. Predictions from ANNs were more accurate than those calculated using non-linear regression. Neuroshell Predictor allowed quantification of the relative importance of the inputs. Furthermore, by varying the network topology and parameters using Neurosolutions, it was possible to obtain output values which were closer to experimental values. Therefore, ANNs represent a promising tool for the analysis of processes involving preparation of polymeric carriers and for prediction of their physical properties.

Drug Compounding↗
PubMed2006

Two-stage fuzzy-stochastic robust programming: a hybrid model for regional air quality management.

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.

Air Pollutants, Occupational↗
PubMed2003

Classification analysis of P-glycoprotein substrate specificity.

Prediction of P-glycoprotein substrate specificity (S(PGP)) can be viewed as a constituent part of a compound's "pharmaceutical profiling" in drug design. This task is difficult to achieve due to several factors that raised many contradictory opinions: (i) the disparity between the S(PGP) values obtained in different assays, (ii) the confusion between Pgp substrates and inhibitors, (iii) the confusion between lipophilicity and amphiphilicity of Pgp substrates, and (iv) the dilemma of describing class-specific relationships when Pgp has no binding sites of high ligand specificity. In this work, we compiled S(PGP) data for 1000 compounds. All data were represented in a binary format, assigning S(PGP) = 1 for substrates and S(PGP) = 0 for non-substrates. Each value was ranked according to the reliability of experimental assay. Two data sets were considered. Set 1 included 220 compounds with S(PGP) from polarized transport across MDR1 transfected cell monolayers. Set 2 included the entire list of 1000 compounds, with S(PGP) values of generally lower reliability. Both sets were analysed using a stepwise classification structure-activity relationship (C-SAR) method, leading to derivation of simple rules for crude estimation of S(PGP) values. The obtained rules are based on the following factors: (i) compound's size expressed through molar weight or volume, (ii) H-accepting given by the Abraham's beta (that can be crudely approximated by the sum of O and N atoms), and (iii) ionization given by the acid and base pKa values. Very roughly, S(PGP) can be estimated by the "rule of fours". Compounds with (N + O) > or = 8, MW > 400 and acid pKa > 4 are likely to be Pgp substrates, whereas compounds with (N + O) < or = 4, MW < 400 and base pKa < 8 are likely to be non-substrates. The obtained results support the view that Pgp functioning can be compared to a complex "mini-pharmacokinetic" system with fuzzy specificity. This system can be described by a probabilistic version of Abraham's solvation equation, suggesting a certain similarity between Pgp transport and chromatographic retention. The chromatographic model does not work in the case of "marginal" compounds with properties close to the "global" physicochemical cut-offs. In the latter case various class-specific rules must be considered. These can be associated with the "amphiphilicity" and "biological similarity" of compounds. The definition of class-specific effects entails construction of the knowledge base that can be very useful in ADME profiling of new drugs.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2003

Fuzzy structure-activity relationships.

While quantitative structure-activity relationships attempt to predict the numerical value of the activities, it is found that statistically good predictors do not always do a good job of qualitatively determining the activity. This study shows how Fuzzy classifiers can be used to generate Fuzzy structure-activity relationships which can more accurately determine whether or not a compound will be highly inactive, moderately inactive or active, or highly active. Four examples of these classifiers are presented and applied to a well-studied activity dataset.

Fuzzy Logic↗
PubMed2006

QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions.

The principle of using a singe model to predict the toxicity of mixtures of chemicals based on the characterisation of the degrees of similarity and dissimilarity of the constituent chemicals using descriptors has been demonstrated in a previous work. The current study introduces a feature extraction technique, independent component analysis, to the method to remove the correlations and dependencies between descriptors and reduce the dimension prior to similarity and dissimilarity calculations. In addition, a goal attainment multi-objective optimisation technique is used for the determination of the fuzzy membership function parameters. For three mixtures, which include a new mixture and two previously studied mixtures that all inhibit reproduction (via different mechanisms of action) in green freshwater algae scenedesmus vacuolatus, the approach showed better or equivalent prediction performance than either concentration addition or independent action models. Unlike QSARs for pure compounds that require large collections of data, the new approach for mixtures only requires one mixture at a particular composition to determine the necessary fuzzy membership function parameter values. These values can then be used to predict the toxicity of the mixture at any other compositions. This could potentially lead to a reduction in the frequency of bioassay tests. Use of the fuzzy membership functions and parameter values obtained for one mixture when used to predict the toxicity of a completely different mixture is also tested and it is found that the approach also gives prediction results with good accuracy.

Chlorophyta↗
PubMed2002

Psychological cue use and implications for a clinical decision support system.

Effective clinical decision making depends upon identifying possible outcomes for a patient, selecting relevant cues, and processing the cues to arrive at accurate judgements of each outcome's probability of occurrence. These activities can be considered as classification tasks. This paper describes a new model of psychological classification that explains how people use cues to determine class or outcome likelihoods. It proposes that clinicians respond to conditional probabilities of outcomes given cues and that these probabilities compete with each other for influence on classification. The model explains why people appear to respond to base rates inappropriately, thereby overestimating the occurrence of rare categories, and a clinical example is provided for predicting suicide risk. The model makes an effective representation for expert clinical judgements and its psychological validity enables it to generate explanations in a form that is comprehensible to clinicians. It is a strong candidate for incorporation within a decision support system for mental-health risk assessment, where it can link with statistical and pattern recognition tools applied to a database of patients. The symbiotic combination of empirical evidence and clinical expertise can provide an important web-based resource for risk assessment, including multi-disciplinary education and training.

Bias↗