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PubMed2006

Exploring series of multivariate censored temporal data through fuzzy coding and correspondence analysis.

We present an exploratory analysis methodology for a multiple series of multivariate temporal data subject to censoring, and thus requiring the introduction of a coding technique: fuzzy coding preserving a large amount of the distributional information is fully adapted. Correspondence analysis is performed on the array produced by fuzzy coding. Projections of mean paths on factorial mappings, according to subgroup characteristics, highlight the behaviour of the underlying process. This approach is illustrated with an application to a randomized controlled clinical trial designed for comparing non-diabetic chronic renal failure treatments. Our methodology has resulted in the identification of a difference between the treatments with an interpretation of the effects in subgroups of patients not obtainable with traditional survival methodology; it also provides some valuable insights for designing further studies on treatment of renal impairment.

Angiotensin-Converting Enzyme Inhibitors↗
PubMed1993

Artificial neural networks as adjuncts for assessing medical students' problem solving performances on computer-based simulations.

Artificial neural networks were trained by supervised learning to recognize the test selection patterns associated with students' successful solutions to seven immunology computer-based simulations. New test selection patterns evaluated by the trained neural network were correctly classified as successful or unsuccessful solutions to the problem > 90% of the time. The examination of the neural networks output weights after each test selection revealed a progressive and selective increase for the relevant problem suggesting that a successful solution is represented by the neural network as the accumulation of relevant tests. Unsuccessful problem solutions were classified by the neural network software into two patterns of students performance. The first pattern was characterized by low neural network output weights for all seven problems reflecting extensive searching and lack of recognition of relevant information. In the second pattern, the output weights from the neural network were biased toward one of the remaining six incorrect problems suggesting that the student misrepresented the current problem as an instance of a previous problem. Finally, neural network analysis could detect cases where the students switched hypotheses during the problem solving exercises.

California↗
PubMed1994

An expert system for the analysis and interpretation of evoked potentials based on fuzzy classification: application to brainstem auditory evoked potentials.

EPEXS is an expert system for evoked potential analysis and interpretation (a medical examination performed in clinical neurophysiology laboratories), working from available clinical records and numerical data extracted from evoked potential traces. EPEXS integrates two formalisms of knowledge representation: rules and structured objects. The rules represent the elementary concepts (shallow knowledge) and include a model of possibility based on the Dubois and Prade default reasoning and possibility theory. The structured objects (prototypes) are organized as hierarchical taxonomies (underlying knowledge). These allow the description of both the objects and their relationships. The heuristics used to interpret knowledge are based on two hypotheses: the unicity of the pathological process leading to several given symptoms and the progression from the general to the specific, leading to the adoption or rejection of a class of diagnoses. This avoids the problem of the differential diagnosis. These sources of knowledge are used in a dynamical way that could be described as a four-step process: acquisition of clinical data in order to define the nosological frame of the pathology, production of hypotheses about the nature and topography of lesions, interpretation of data in accordance with these hypotheses, and finally evaluation of their likelihood. The validation shows that EPEXS topographic diagnoses were correct in 100% of cases and 92% of it nosologic diagnoses were correct, and no pathological record was interpreted as normal. When examined on a given pathology basis EPEXS was not significantly different from human experts as regards to performance, specificity, and sensitivity.

Computer Simulation↗
PubMed1995

Fuzzy sets for feature identification in biomedical signals with self-assessment of reliability: an adaptable algorithm modeling human procedure in BAEP analysis.

The analysis of biomedical signals has mostly been restricted to traditional signal-processing methods. This article proposes a different approach, applied to the evaluation of brainstem auditory evoked potentials. An automatic peak identification method is described which uses criteria applied by human evaluators in visual analysis. These criteria are defined as fuzzy sets and are combined using fuzzy operations, thus reflecting the weighting of different facts by humans. Membership values are interpreted as degrees of satisfaction which indicate the degree to which a sample satisfies a given criterion. The system judges its own performance in terms of degrees of reliability. Tests on a large set of clinical data showed high performance on good and average-quality curves. A substantial drawback was the assignment of too many peaks in poor potentials. The approaches presented here can easily be applied to similar one-dimensional (and higher) signal analysis tasks.

Algorithms↗
PubMed1996

Segmentation of multispectral magnetic resonance image using penalized fuzzy competitive learning network.

Segmentation (tissue classification) of the medical images obtained from Magnetic resonance (MR) images is a primary step in most applications of computer vision to medical image analysis. This paper describes a penalized fuzzy competitive learning network designed to segment multispectral MR spin echo images. The proposed approach is a new unsupervised and winner-takes-all scheme based on a neural network using the penalized fuzzy clustering technique. Its implementation consists of the combination of a competitive learning network and penalized fuzzy clustering methods in order to make parallel implementation feasible. The penalized fuzzy competitive learning network could provide an acceptable result for medical image segmentation in parallel processing using the hardware implementation. The experimental results show that a promising solution can be obtained using the penalized fuzzy competitive learning neural network based on least squares criteria.

Adult↗
PubMed1993

Evaluation of the critical value of driving fatigue based on the fuzzy sets theory.

Applying the newly developed multistage evaluation method based on the fuzzy sets theory, we have evaluated the sense of driving fatigue of 8 male drivers who covered 400 km in 8 hr in the daytime, empirically revealing the distribution of the degree of driving fatigue. The critical values from "no" to "a bit" and "a bit" to "fatigued" are 1 and 5.5 hr, respectively. Finally, a tentative study of the psychophysical relations between driving time and driving fatigue has been made, leading to a curve of driving fatigue under the conditions of this experiment.

Adult↗
PubMed2001

A hybrid input-output approach to model metabolic systems: an application to intracellular thiamine kinetics.

Models of the dynamics of complex metabolic systems offer potential benefits to the deep comprehension of the system under study as well as for the performance of certain tasks. Unfortunately, dynamic modeling of a great deal of metabolic systems may be problematic due to the incompleteness of the available knowledge about the underlying mechanisms and to the lack of an adequate observational data set. In theory, a valid alternative to classical structural modeling through ordinary differential equations could be represented by input-output approaches. But, in practice, such methods, which learn the nonlinear dynamics of the system from input-output data, fail when the experimental data set is poor either in size or in quality. Such a situation is not rare in the case of metabolic systems. This paper deals with a hybrid approach which aims at overcoming the problems addressed above. More specifically, it allows us to solve the identification problems of the intracellular thiamine kinetics in the intestine tissue. The method, which is half way between the structural and input-output approach, uses the outcomes of the simulation of a qualitative structural model to build a good initialization of a fuzzy system identifier. Such an initialization allows us to efficiently cope with both the incompleteness of knowledge and the inadequacy of the available data set, and to derive an input-output model of the intracellular thiamine kinetics in the intestine tissue. The comparison of the predictions of the intracellular thiamine kinetics obtained by the application of such a model with those obtained by traditional approaches, namely compartmental models, neural networks, and fuzzy systems, highlighted a better performance of our model. As the structural assumptions are relaxed, we obtained a model slightly less informative than a purely structural one but robust enough to be used as a simulator. The paper also discusses the interpretative potential offered by such a model, as tested on diabetic subjects.

Animals↗
PubMed2001

Identification of river water quality using the fuzzy synthetic evaluation approach.

Proper identification of water quality conditions in a river system based on limited observations is an essential task for meeting the goals of environmental management. Various classification methods have been used for estimating the changing status and usability of surface water in river basins. However, a discrepancy frequently arises from the lack of a clear distinction between each water utilisation mode, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated water quality conditions with respect to various chemical constituents, biological aspects, nutrients, and aesthetic qualities. This paper presents a comparative study using three fuzzy synthetic evaluation techniques to assess water quality conditions in comparison to the outputs generated by conventional procedures such as the Water Quality Index (WQI). Based on a set of data collected at seven sampling stations, a case study for the Tseng-Wen River system in Taiwan was used to demonstrate their application potential. The findings clearly indicate that the techniques may successfully harmonise inherent discrepancies and interpret complex conditions. A further, newly developed fuzzy synthetic evaluation approach described in this paper might also be useful for verifying water quality conditions for the Total Maximum Daily Load (TMDL) program and be helpful for constructing an effective water quality management strategy.

Conservation of Natural Resources↗
PubMed2002

Assessment and prediction of overall environmental quality of Zhuzhou City, Hunan Province, China.

Fuzzy comprehensive assessment was applied to assess the quality of air, water and soil in Zhuzhou City, Hunan Province, China based on the monitoring data of 1997 and National Environmental Quality Standards of China. Results show that Zhuzhou air quality belonged to Class II with industry and traffic as the main control areas and SO2 as the major pollutant. Water quality within the Zhuzhou section of the Xiang River belonged to Class IV, with the Xiawan section as the main control area and NH3-N as the major pollutant. Soil environmental quality with respect to heavy metal pollution belonged to Class III, with Cd as the major pollutant. For those pollutants showing significant differences between the years of 1990-1997, predictions were made for the environmental quality for the period 1998-2003. According to the assessment results, cleaner production in the key pollution sources and the establishment of a municipal sewage treatment plant are proposed to improve the environmental quality of Zhuzhou City.

China↗
PubMed2007

Three-dimensional segmentation of the tumor in computed tomographic images of neuroblastoma.

Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, and normal tissue are often intermixed. Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to therapy and in the planning of the delayed surgery for resection of the tumor. We propose methods to achieve 3-dimensional segmentation of the neuroblastic tumor. In our scheme, some of the normal structures expected in abdominal CT images are delineated and removed from further consideration; the remaining parts of the image volume are then examined for tumor mass. Mathematical morphology, fuzzy connectivity, and other image processing tools are deployed for this purpose. Expert knowledge provided by a radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are incorporated into the segmentation algorithm. In this preliminary study, the methods were tested with 10 CT exams of four cases from the Alberta Children's Hospital. False-negative error rates of less than 12% were obtained in eight of 10 exams; however, seven of the exams had false-positive error rates of more than 20% with respect to manual segmentation of the tumor by a radiologist.

Brain Neoplasms↗
PubMed2005

Generalised overlap measures for assessment of pairwise and groupwise image registration and segmentation.

Effective validation techniques are an essential pre-requisite for segmentation and non-rigid registration techniques to enter clinical use. These algorithms can be evaluated by calculating the overlap of corresponding test and gold-standard regions. Common overlap measures compare pairs of binary labels but it is now common for multiple labels to exist and for fractional (partial volume) labels to be used to describe multiple tissue types contributing to a single voxel. Evaluation studies may involve multiple image pairs. In this paper we use results from fuzzy set theory and fuzzy morphology to extend the definitions of existing overlap measures to accommodate multiple fractional labels. Simple formulas are provided which define single figures of merit to quantify the total overlap for ensembles of pairwise or groupwise label comparisons. A quantitative link between overlap and registration error is established by defining the overlap tolerance. Experiments are performed on publicly available labeled brain data to demonstrate the new measures in a comparison of pairwise and groupwise registration.

Algorithms↗
PubMed2005

Fuzzy pattern classification of hemodynamic data can be used to determine noninvasive intracranial pressure.

OBJECTIVE: The authors previously introduced a method in which intracranial pressure (ICP) was estimated using parameters (TCD characteristics) derived from cerebral blood flow velocity (FV) and arterial blood pressure (ABP). Some results suggested that this model might be influenced by the patient's state of cerebral autoregulation and other clinical parameters. Hence, it was the aim of the present study to improve the method by modifying the previously used global procedure in certain subgroups of patients. METHODS: In 103 traumatic brain injured patients (3-76 years, mean: 31 +/- 16 years) signal data of FV, ABP and ICP were used to generate samples of TCD characteristics together with time corresponding ICP. Fuzzy Pattern Classification was used to identify cluster subsets (classes) of the sample space. On each class a local estimator of ICP was defined. This approach provides a non-invasive assessment of ICP (nICP) as follows: Using FV and ABP the TCD characteristics were computed and related to the matching classes. nICP was calculated as a weighted sum of local ICP estimations. RESULTS: ICP A and B waves and long-term trends could be visibly assessed. The median absolute difference between ICP and nICP was 5.7 mmHg. CONCLUSIONS: The class structure of the model facilitates nICP assessment in heterogeneous patient groups and supports a stepwise extension of the target patient group without affecting the former validity.

Adolescent↗
PubMed2003

CAD tool for burn diagnosis.

In this paper a new system for burn diagnosis is proposed. The aim of the system is to separate burn wounds from healthy skin, and the different types of burns (burn depths) from each other, identifying each one. The system is based on the colour and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. We use a perceptually uniform colour space (L*u*v*), since Euclidean distances calculated in this space correspond to perceptually colour differences. After the burn is segmented, some colour and texture descriptors are calculated and they are the inputs to a Fuzzy-ARTMAP neural network. The neural network classifies them into three types of bums: superficial dermal, deep dermal and full thickness. Clinical effectiveness of the method was demonstrated on 62 clinical burn wound images obtained from digital colour photographs, yielding an average classification success rate of 82% compared to expert classified images.

Algorithms↗
PubMed2004

Industrial application of fuzzy control in bioprocesses.

In a bioprocess, for example a fermentation process, many biological reactions are always working in intracellular space and the control of such a process is very complicated. Bioprocesses have therefore been controlled by the judgment of the experts who are the skilled operators and have much experience in the control of such processes. Such experience is normally described in terms of linguistic IF-THEN rules. Fuzzy inference is a powerful tool for incorporating linguistic rules into computer control of such processes. Fuzzy control is divided into two types--direct fuzzy control of process variables, for example sugar feed rate and fermentation temperature, and indirect control via phase recognition. In bioprocess control the experts decide the value of controllable process variables such as sugar feed rate or temperature as output data from several state variables as input data. Fuzzy control is regarded as a computational algorithm in which the causal relationship between input and output data are incorporated. In Japan fuzzy control has already been applied to practical industrial processes such as production of pravastatin precursor and vitamin B2 and to the Japanese sake mashing process; these examples are reviewed. In addition, an advanced control tool developed from a study on fuzzy control, fuzzy neural networks (FNN), are introduced. FNN can involve complicated causality between input and output data in a network model. FNN have been proven to be applicable to a research in biomedicine, for example modeling of the complicated causality between electroencephalogram or gene expression profiling data and prognostic prediction. Successful results on this research will be also explained.

Algorithms↗
PubMed2004

Recent progress in microbial cultivation techniques.

Recent advances in the improvement of microbial cultivation are reviewed, with emphasis on biochemical engineering techniques as a means of obtaining high production rate of bioproduct. Possible uses of high cell density culture include their use in food industry as well as in the production of new medicines and in biotechnology. Concentration of microorganisms using a hollow fiber membrane or centrifuge, and increase in cell density by controlling the pH, dissolved oxygen, or carbon source concentrations of the culture broth with control algorithms are discussed. In a culture of filamentous microorganisms the mycelial morphology is hard to define and it is difficult to quantify its amount, and this is one of the bottlenecks hampering the improvement of production rate. Specific features of mycelial cultivation in the presence of highly pulpy mycelia and entangled-pellets are scrutinized by visual inspection through a microscope that is linked to a computer, and using software that can characterize the mycelial morphology. Image analysis technology for analyzing the mycelial image captured by a digital camera is a potential tool for morphological analysis, including analysis of the morphological development of filamentous microorganisms.

Algorithms↗
PubMed2004

Application of knowledge information processing methods to biochemical engineering, biomedical and bioinformatics fields.

In biochemical and biomedical engineering fields there are a variety of phenomena with many complex chemical reactions, in which many genes and proteins affect transcription or enzyme activity of others. It is difficult to analyze and estimate many of these phenomena using conventional mathematical models. Recently some knowledge information processing methods, such as the artificial neural network (ANN), fuzzy reasoning, fuzzy neural network (FNN), fuzzy adaptive resonance theory (fuzzy ART) and the genetics algorithm (GA), were developed in the computer science field and have been applied to analysis in a variety of research fields. In this chapter, these methods will be briefly reviewed. Next, the application of these methods in the biochemical field will be introduced, instancing two examples in actual industrial processes. In addition, the application in the biomedical and bioinformatics field as another attractive field will be reviewed. Two examples are our research such as the prediction of prognosis for cancer patients from DNA microarray data using FNN and gene clustering for DNA microarray data using fuzzy ART.

Artificial Intelligence↗
PubMed1992

Object-oriented fuzzy expert system for on-line diagnosing and control of bioprocesses.

An object-oriented fuzzy expert system to support on-line control of an automated fermentation plant is described. The major elements of the system consist of a fuzzy inference engine, a database, a knowledge base, and an expression evaluater. The expression evaluater calculates specific rates for growth, and substrate and product formation at different physiological states during the cultivation from the measured data. The specific rates are then compared with the standard target rates stored in the database. If differences outside the set tolerances were observed, the inference engine analyses the reasons for the faults on the basis of the knowledge represented in the form of a knowledge network and fuzzy membership functions of the process variables. The fuzzy expert system was developed on the basis of a shell constructed by using the object oriented Smalltalk/V Mac programming environment, with Lac-tobacillus casei lactic acid fermentation as the example of process application.

Biotechnology↗