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Results for “Fuzzy Logic”

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At least 181 records · Page 10Linked to original sources
PubMed2004

Perception of disability in a public health perspective: a model based on fuzzy logic.

Measures of functional levels, commonly used to assess the safety and quality of life of individuals and populations, have not yet been derived from a fuzzy framework. The aim of this study is to estimate the degree of disability associated with varying functional levels, through a model based on fuzzy sets theory. A fuzzy linguistic model was developed to measure varying levels of functional disability, in accordance with the definitions of an individual's social and physical activities and mobility. One year of an adult's life whose mobility, social and physical activities were somewhat limited, was judged to be equivalent to 0.575 years free of functional disability. Results obtained from the fuzzy model approach those obtained with the quality of well-being scale (QWB), used as a conceptual framework. Such findings are encouraging, since the QWB is considered a consistent and valid approach for disability assessment and quality-of-life evaluation.

Adolescent↗
PubMed1999

A fuzzy logic approach to identifying brain structures in MRI using expert anatomic knowledge.

We report a novel computer method for automatic labeling of structures in 3D MRI data sets using expert anatomical knowledge that is coded in fuzzy sets and fuzzy rules. The method first identifies major structures and then uses spatial relationships to these landmarks to recognize other structures. This labeling process simulates the iterative process that we ourselves use to locate structures in images. We demonstrate its application in three data sets, labeling brain MRI by locating the longitudinal and lateral fissures and the central sulci and then determining boundaries for the frontal lobes. Our method is adaptable to the identification of other anatomical structures.

Brain↗
PubMed1999

Identification of causal relations between haemodynamic variables, auditory evoked potentials and isoflurane by means of fuzzy logic.

The aim of this study was to identify a possible relationship between haemodynamic variables, auditory evoked potentials (AEP) and inspired fraction of isoflurane (ISOFl). Two different models (isoflurane and mean arterial pressure) were identified using the fuzzy inductive reasoning (FIR) methodology. A fuzzy model is able to identify non-linear and linear components of a causal relationship by means of optimization of information content of available data. Nine young female patients undergoing hysterectomy under general anaesthesia were included. Mean arterial pressure (MAP), heart rate (HR), end-tidal expired carbon dioxide (CO2ET), AEP and ISOFl were monitored with a sampling time of 10 s. The AEP was extracted using an autoregressive model with exogenous input (ARX model) which decreased the processing time compared with a moving time average. The AEP was mapped into a scalar, termed the depth of anaesthesia index (DAI) normalized to 100 when the patient was awake and descending to an average of 25 during loss of consciousness. The FIR methodology identified those variables among the input variables (MAP, HR, CO2ET, DAI or ISOFl) that had the highest causal relation with the output variables (ISOFl and MAP). The variables with highest causal relation constitute the ISOFl and MAP models. The isoflurane model predicted the given anaesthetic dose with a mean error of 12.1 (SD 10.0)% and the mean arterial pressure model predicted MAP with a mean error of 8.5 (7.8)%.

Anesthetics, Inhalation↗
PubMed2000

[Automated detection of spleen volume by spiral CT scans using neural networks and "fuzzy logic"].

PURPOSE: To assess spleen segmentation and volumentry in spiral CT scans with and without pathological changes of splenic tissue. METHODS: The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. RESULTS: The neural nets achieved a higher accuracy than the thresholding method. Correlation coefficient of the fuzzy-neural nets: 0.99 (thresholding: 0.63). Mean true positive rate: 90% (thresholding: 75%), mean false positive rate: 5% (thresholding > 100%). Pitfalls were caused by accessory spleens, extreme changes in the morphology (tumors, metastases, cysts), and parasplenic masses. CONCLUSIONS: Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans.

Adult↗
PubMed2004

Fuzzy logic detection of medically serious suicide attempt records in major psychiatric disorders.

Clinical prediction of suicide is a complicated task. The focus for improved suicide risk detection is on the subgroup of individuals whose high suicide risk remains unrecognized by clinicians. We sought to evaluate the accuracy of Fuzzy Adaptive Learning Control Network (FALCON) neural networks, a nonlinear algorithm, in identification of this subgroup. The study sample included the Computerized Scale for risk of Suicide, including 21 suicide risk factors (including the target variable) drawn from 987 patient records, completed by staff clinicians during face-to-face interviews of hospitalized patients. FALCON evaluated all records in two steps: a) 612 for training and 375 for validation, and b) 887 for training and 100 for validation. The existence of previous medically serious suicide attempts (MSSAs) was chosen as the target variable because it is generally recognized as the strongest suicide risk factor. Sensitivity, specificity, and unknown answers among MSSA and non-MSSA were as follows: 612/375 FALCON, 91%, 85%, 11%, 15%; 887/100 FALCON, 94%, 82%, 20%, 14.5%, respectively. Trained FALCON, a nonlinear neural network, achieves respectable accuracy in detecting MSSA patients based on 20 suicide risk factors. Trained FALCON may therefore assist in identification of subgroup of individuals who remain unrecognized by clinicians and contribute to prevention of suicide.

Adolescent↗
PubMedDate not supplied

Renoir, Pneumon-IA and Terap-IA: three medical applications based on fuzzy logic.

The research at the IIIA has produced over more than a decade two versions of a tool for developing knowledge-based systems: Milord and Milord II. This tool has been mainly used for the development of medical applications. In this paper we summarize the Milord II approximate reasoning approach based on fuzzy sets, and three medical applications: rheumatology diagnosis (Renoir), pneumonia diagnosis (Pneumon-IA) and pneumonia treatment (Terap-IA).

Artificial Intelligence↗
PubMed1997

Operationalization of clinical practice guidelines using fuzzy logic.

There are a number of obstacles to successful operationalization of clinical practice guidelines, including the difficulty in accurately representing a statement's decidability or an action's executability. Both require reasoning with incomplete and imprecise information, and we present one means of processing such information. We begin with a brief overview of fuzzy set theory, in which elements can have partial memberships in multiple sets. With fuzzy inferencing, these sets can be combined to create multiple conclusions, each with varying degrees of truth. We demonstrate a fuzzy model developed from a published clinical practice guideline on the management of first simple febrile seizures. Although the creation of fuzzy sets can be an arbitrary process, we believe that fuzzy inferencing is an effective tool for the expression of guideline recommendations, and that it can be useful for the management of imprecision and uncertainty.

Age Factors↗
PubMed1994

Fuzzy logic assisted control of inspired oxygen in ventilated newborn infants.

The control of oxygen delivery to mechanically ventilated newborn infants is a time intensive process that must balance adequate tissue oxygenation against possible toxic effects of oxygen exposure. Investigation in computer assisted control of mechanical ventilation is increasing, although very few studies involve newborn infants. We have implemented a fuzzy controller for the adjustment of inspired oxygen concentration (FIO2) in ventilated newborns. The controller utilizes rules produced by neonatologists, and operates in real-time. A clinical trial of this controller is currently taking place in the neonatal intensive care unit (NICU) of Children's Hospital, Boston, MA.

Computer Systems↗
PubMed2002

Using fuzzy logic to confirm the integrity of a pattern recognition algorithm for long genomic sequences: the W-curve.

The W-curve is a numerical mapping algorithm that provides tertiary information content of long and short genomic sequences. The most popular genomic pattern recognition algorithms depend on string matching of the primary information content of short genomic sequences. Herein, we describe a way to define the fuzzy properties of the W-curve. This approach improves a distance (dissimilarity) between two or more homologous long genomic sequences. Fourier analysis of W-curves delivers a smoother function for gap-stripped regions. Calculation of respective Fourier energies may improve the accuracy of the distance metric used to generate a phylogenetic tree of analyzed genomic sequences. This is especially the case for long genomic sequences that have been gap-stripped and aligned with the aid of previously published heuristic methods. These previous methods involved W-curve alignments used in concert with such programs as Clustal that use linear dynamic programming to align multiple gap-stripped W-curves.

Algorithms↗
PubMed1993

[Quantification of the intra-penile smooth muscle with a fuzzy logic algorithm].

We have developed a new computer program for tissue segmentation designed for the quantification of smooth muscle in the corpus cavernosum. The program uses digitalized images of stained histological sections taken with a CCD camera. The recognition of the different tissues is based on the examination of absorption of monochromatic light. The section is successively illuminated with light of three different wavelengths and the three absorption values and texture parameters are used for the identification of the tissue using a fuzzy algorithm.

Absorption↗