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PubMed2003

Characterization of medical time series using fuzzy similarity-based fractal dimensions.

This paper attempts to characterize medical time series using fractal dimensions. Existing fractal dimensions like box, information and correlation dimensions characterize the time series by measuring the rate at which the distribution of the time series changes when the length (or radius) of the box (or hypersphere) is changed. However, the measured dimensions significantly vary when the box (or hypersphere) position is changed slightly. It happens because the data points just outside the box (or hypersphere) are not accounted for, and all the data points inside the box or hypersphere are treated equally. To overcome these problems, the hypersphere is converted to a Gaussian, and thus the hard boundary becomes soft. The Gaussian represents the fuzzy similarity between the neighbors and the point around which the Gaussian is constructed. This concept of similarity is exploited to propose a fuzzy similarity-based fractal dimension. The proposed dimension aims to capture the regularity of the time series in terms of how the fuzzy similarity scales up/down when the resolution of the time series is decreased/increased. Experiments on intensive care unit (ICU) data sets show that the proposed dimension characterizes the time series better than the correlation dimension.

Algorithms↗

PubMed2003

Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions.

In this contribution we present an application of a knowledge-based neural network technique in the domain of medical research. We consider the crucial problem of intensive care patients developing a septic shock during their stay at the intensive care unit. Septic shock is of prime importance in intensive care medicine due to its high mortality rate. Our analysis of the patient data is embedded in a medical data analysis cycle, including preprocessing, classification, rule generation and interpretation. For classification and rule generation we chose an improved architecture based on a growing trapezoidal basis function network for our metric variables. Our results extend those of a black box classification and give a deeper insight in our patient data. We evaluate our results with classification and rule performance measures. For feature selection we introduce a new importance measure.

Abdomen↗

PubMed1997

Fuzzy set theory in medicine.

Indeed, the complexity of biological systems may force us to alter in radical ways our traditional approaches to the analysis of such systems. Thus, we may have to accept as unavoidable a substantial degree of fuzziness in the description of the behavior of biological systems as well as in their characterization. This fuzziness, distasteful though it may be, is the price we have to pay for the ineffectiveness of precise mathematical techniques in dealing with systems comprising a very large number of interacting elements or involving a large number of variables in their decision trees.

Artificial Intelligence↗

PubMed1997

Lightweight fuzzy processes in clinical computing.

In spite of advances in computing hardware, many hospitals still have a hard time finding extra capacity in their production clinical information system to run artificial intelligence (AI) modules, for example: to support real-time drug-drug or drug-lab interactions; to track infection trends; to monitor compliance with case specific clinical guidelines; or to monitor/ control biomedical devices like an intelligent ventilator. Historically, adding AI functionality was not a major design concern when a typical clinical system is originally specified. AI technology is usually retrofitted 'on top of the old system' or 'run off line' in tandem with the old system to ensure that the routine work load would still get done (with as little impact from the AI side as possible). To compound the burden on system performance, most institutions have witnessed a long and increasing trend for intramural and extramural reporting, (e.g. the collection of data for a quality-control report in microbiology, or a meta-analysis of a suite of coronary artery bypass grafts techniques, etc.) and these place an ever-growing burden on typical the computer system's performance. We discuss a promising approach to adding extra AI processing power to a heavily-used system based on the notion 'lightweight fuzzy processing (LFP)', that is, fuzzy modules designed from the outset to impose a small computational load. A formal model for a useful subclass of fuzzy systems is defined below and is used as a framework for the automated generation of LFPs. By seeking to reduce the arithmetic complexity of the model (a hand-crafted process) and the data complexity of the model (an automated process), we show how LFPs can be generated for three sample datasets of clinical relevance.

Biopsy, Needle↗

PubMed1997

Application of the fuzzy ART/MAP and MINMAX/MAP neural network models to radiographic image classification.

This paper concerns the classification analysis of exercise-induced lower leg pain by applying competitive neural network clustering and mapping techniques to type 1 and type 2 fuzzy descriptions of bone scan images of the tibia. The clusters are described and compared with each other and with the experts known classes that would be expected from medical findings. The discovered clusters provide training sets for supervised learning by an ARTMAP and similar neural network. These were used to classify the previously unclassified images and hence improve the classification process. The overall conclusion is that the use of the neural clustering methods has improved the classification process of the shin images despite the paucity of data and its inherent uncertainty.

Athletic Injuries↗

PubMed1997

Application of autonomous neural network systems to medical pattern classification tasks.

This paper presents a study of the application of autonomously learning multiple neural network systems to medical pattern classification tasks. In our earlier work, a hybrid neural network architecture has been developed for on-line learning and probability estimation tasks. The network has been shown to be capable of asymptotically achieving the Bayes optimal classification rates, on-line, in a number of benchmark classification experiments. In the context of pattern classification, however, the concept of multiple classifier systems has been proposed to improve the performance of a single classifier. Thus, three decision combination algorithms have been implemented to produce a multiple neural network classifier system. Here the applicability of the system is assessed using patient records in two medical domains. The first task is the prognosis of patients admitted to coronary care units; whereas the second is the prediction of survival in trauma patients. The results are compared with those from logistic regression models, and implications of the system as a useful clinical diagnostic tool are discussed.

Artificial Intelligence↗

PubMed1999

Advances in fuzzy theory.

Recent advances in the foundations of fuzzy theory facilitated by Bart Kosko's discovery of the fuzzy hypercube are of high relevance to artificial intelligence in medicine research. The new concepts of set inclusion, equality, and similarity are therefore reported in this paper. A unifying notion of a Lotfi Zadeh space is also introduced to show that Bart Kosko's fuzzy hypercube is a Zadeh space.

Fuzzy Logic↗

PubMed1999

Fuzzy diagnosis.

Explore the source record for details and available documents.

Diagnosis, Computer-Assisted↗

PubMed1999

A fuzzy clustering based segmentation system as support to diagnosis in medical imaging.

In medical imaging uncertainty is widely present in data, because of the noise in acquisition and of the partial volume effects originating from the low resolution of sensors. In particular, borders between tissues are not exactly defined and memberships in the boundary regions are intrinsically fuzzy. Therefore, computer assisted unsupervised fuzzy clustering methods turn out to be particularly suitable for handling a decision making process concerning segmentation of multimodal medical images. By using the possibilistic c-means algorithm as a refinement of a neural network based clustering algorithm named capture effect neural network, we developed the possibilistic neuro fuzzy c-means algorithm (PNFCM). In this paper the PNFCM has been applied to two different multimodal data sets and the results have been compared to those obtained by using the classical fuzzy c-means algorithm. Furthermore, a discussion is presented about the role of fuzzy clustering as a support to diagnosis in medical imaging.

Algorithms↗

PubMed1999

Obtaining interpretable fuzzy classification rules from medical data.

For many application problems classifiers can be used to support a decision making process. In some domains-in areas like medicine especially-it is preferable not to use black box approaches. The user should be able to understand the classifier and to evaluate its results. Fuzzy rule based classifiers are especially suitable, because they consist of simple linguistically interpretable rules and do not have some of the drawbacks of symbolic or crisp rule based classifiers. Classifiers must often be created from data by a learning process, because there is not enough expert knowledge to determine their parameters completely. A simple and convenient way to learn fuzzy classifiers from data is provided by neuro-fuzzy approaches. In this paper we discuss extensions to the learning algorithms of neuro-fuzzy classification (NEFCLASS), a neuro-fuzzy approach for data analysis that we have presented before. We present interactive strategies for pruning rules and variables from a trained classifier to enhance its readability, and demonstrate our approach on a small example.

Algorithms↗

PubMed1999

Fuzzy pre-processing of gold standards as applied to biomedical spectra classification.

Fuzzy gold standard adjustment is a novel fuzzy set theoretic pre-processing strategy that compensates for the possible imprecision of a well-established gold standard (reference test) by adjusting, if necessary, the class labels in the design set while maintaining the gold standard's discriminatory power. The adjusted gold standard incorporates robust within-class centroid information. This strategy was applied to biomedical data acquired from a MR spectrometer for the purpose of classifying human brain neoplasms. It is shown that consistent improvement (10-13%) to the discriminatory power of the underlying classifier is obtained when using this pre-processing strategy.

Astrocytoma↗

PubMed1999

Fundamentals of clinical methodology: 3. Nosology.

Intelligent clinical judgement is one of the main goals of medical artificial intelligence. To achieve that goal requires a reliable nosological language. Such a language is not yet available, however. In this paper the notions of health, disease, and nosology are analyzed. It is shown that health and disease as generic concepts, and also individual disease entities are best understood as fuzzy sets. Clinical language and linguistics, nosology and diagnosis may thus become directly amenable to fuzzy theory.

Artificial Intelligence↗

PubMed1999

A fuzzy-genetic approach to breast cancer diagnosis.

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies-fuzzy systems and evolutionary algorithms-so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting two prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable.

Algorithms↗

PubMed2000

Fuzzy genomes.

A metric space - dubbed the fuzzy polynucleotide space - is presented for diagnostic purposes in the widest sense to measure the degree of difference and similarity between sequences of nucleic acids. To this end, these acids are transformed to ordered fuzzy sets. They thus become representable as points in n-dimensional unit hypercubes that may be endowed with various metrics. In this way, genetic information in particular and genetics in general become amenable to fuzzy theory, geometry, and topology.

Amino Acids↗

PubMed2000

Automatic analysis of signals with symbolic content.

This paper presents a set of methods for helping in the analysis of signals with particular features that admit a symbolic description. The methodology is based on a general discrete model for a symbolic processing subsystem, which is fuzzyfied by means of a fuzzy inference system. In this framework a number of design problems have been approached. The curse of dimensionality problem and the specification of adequate membership functions are the main ones. In addition, other strategies, which make the design process simpler and more robust, are introduced. Their goals are automating the production of the rule base of the fuzzy system and composing complex systems from simpler subsystems under symbolic constrains. These techniques are applied to the analysis of wakefulness episodes in the sleep EEG. In order to solve the practical difficulty of finding remarkable situations from the outputs of the symbolic subsystems an unsupervised adaptive learning technique (FART network) has been applied.

Artificial Intelligence↗

PubMed1996

Fuzzy sequences, specific attachments? Chromosome dynamics.

The assembly of condensed chromosomes in a cell-free system is inhibited by the addition of proteins that bind AT-rich DNA. Does this implicate the AT-rich scaffold attachment regions (SARs) in the formation of chromosomes?

Cell-Free System↗

PubMed2001

A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods.

In recent years, several new approaches to gait data analysis have been explored, including fuzzy systems, multivariate statistical techniques and fractal dynamics. Through a critical survey of recent gait studies, this paper reviews the potential of these methods to strengthen the gait laboratory's analytical arsenal. It is found that time-honoured multivariate statistical methods are the most widely applied and understood. Although initially promising, fuzzy and fractal analyses of gait data remain largely unknown and their full potential is yet to be realized. The trend towards fusing multiple techniques in a given analysis means that additional research into the application of these two methods will benefit gait data analysis.

Electromyography↗

PubMed2001

Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

PURPOSE: This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. METHODS: A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. RESULTS: The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. CONCLUSIONS: It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

Brazil↗