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At least 1,603 records · Page 89Linked to original sources
PubMed2005

Prediction of burn healing time using artificial neural networks and reflectance spectrometer.

BACKGROUND: Burn depth assessment is important as early excision and grafting is the treatment of choice for deep dermal burn. Inaccurate assessment causes prolonged hospital stay, increased medical expenses and morbidity. Based on reflected burn spectra, we have developed an artificial neural network to predict the burn healing time. PURPOSE: Our study is to develop a non-invasive objective method to predict burn-healing time. METHODS AND MATERIALS: Burns less than 20% TBSA was included. Burn spectra taken on the third postburn day using reflectance spectrometer were analyzed by an artificial neural network system. RESULTS: Forty-one spectra were collected. With the newly developed method, the predictive accuracy of burns healed in less than 14 days was 96%, and that in more than 14 days was 75%. CONCLUSIONS: Using reflectance spectrometer, we have developed an artificial neural network to determine the burn healing time with 86% overall predictive accuracy.

Adolescent↗
PubMedDate not supplied

Neural network detection of files of suicidal patients and suicidal profiles.

The optimal configuration of backpropagation (BP) neural networks was determined after 35 trials with different BP configurations evaluating the total detection rate. Ten different training and testing sets were used to identify optimal samples. All trials included sample files of patients with medically serious suicidal attempts (MSSA) and those of non-suicidal patients. Fifty files were used in each group for training and 49 files for testing with no overlap between the samples. The target variable for training was seriousness of suicide attempt (0 = non-suicidal, 1 = MSSA). The input set included 44 demographic, clinical and patient-history variables. The optimal results showed that 93.8% of MSSA and 89.8% of the non-suicidal patient files were detected. Total success rate (TSR) was 91.8% and positive and negative prediction values (PPV, NPV) were 92% and 95.6%, respectively. Living alone (6.76), treatment compliance (5.86), drug abuse or dependence (2.8), global assessment of functioning (GAF) score (1.49), non-paranoid delusions (1.22) and suicide of first degree relative (1.1) were highly associated with MSSA according to the Garson calculation. However, logistic regression attributed high importance to hallucinations (p < 0.0001), diagnosis (p < 0.002), number of children (p < 0.006), GAF score (p < 0.006), employment status (p < 0.02) and stressors (p < 0.03). It was shown that: backpropagation neural networks are very successful in identifying records of MSSA patients; a high GAF score is associated with high risk of MSSA and is the only common variable identified by both methods; and backpropagation identified two non-specific factors (living alone and treatment compliance) whereas statistics found specific factors (hallucinations and diagnosis) highly associated with MSSA.

Adolescent↗
PubMed1996

A neural network simulation of the vestibular system: implications on the role of intervestibular nuclear coupling during vestibular compensation.

Previous neural network simulations of the vestibular system have been based loosely on known physiology. This research involved the use of a strongly physiologically based neural network model which was used to investigate the role of the vestibular commissure in restoring the bilateral symmetry of the resting rates of the vestibular nuclei during vestibular compensation following unilateral labyrinthectomy. It was found the readjustments in the gain of the vestibular commissure were not primarily responsible for vestibular compensation, as has previously been suggested, but rather that it was modifications in extralabyrinthine sources of tone which mediated the restoration of the central symmetry between the two nuclei.

Animals↗
PubMed2000

Automated scoring of patient pain drawings using artificial neural networks: efforts toward a low back pain triage application.

The goal of this research was to examine methods of automatically scoring patient pain drawings. Two hundred and fifty pain drawings were selected from the files of an orthopaedic surgeon who specializes in the treatment of low back pain patients. An artificial neural network was designed to score these drawings. The drawings were segmented into 85 regions following dermatomal mappings and from these regions the percent area in pain in each was computed and used as the neural network input variables. With five outcome categories (scores) we obtained a classification sensitivity of 49%, which is approximately as well as physician experts and discriminant analysis achieved using a subset of the same data. We conclude that an artificial neural network is well suited to automatically score patient pain drawings.

Humans↗
PubMedDate not supplied

Comparison between neural networks (NN) and principal component analysis (PCA): structure activity relationships of 1,4-dihydropyridine calcium channel antagonists (nifedipine analogues).

The applicability of the neural network computer package PSDD (Perceptron Simulator for Drug Design/Perceptron-type Neural Network Simulator) in structure-activity relationship (SAR) studies was investigated. A group of 1,4-dihydropyridine derivatives was used in order to compare the PSDD results with those obtained previously with PCA. Calculated atomic and molecular descriptors using the semiempirical AM1 method were mainly used. It was shown that the predictive capability demonstrated by PSDD in SAR analysis were almost equivalent to that of PCA.

Algorithms↗
PubMed1998

Neural networks as "software sensors" in enzyme engineering.

Industrial applications of enzyme technology are rapidly increasing. On-line control of enzyme production processes, however, is difficult owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables and quality attributes. We demonstrate that well-trained feedforward backpropagation neural networks with one hidden layer can be employed to overcome such problems with no need for a priori knowledge of the relationships of the process variables involved. Neural network programs were written in Microsoft Visual C++ for Windows and implemented in a personal computer. The goodness of fit of the trained neural network to the reference data was determined by the coefficient of determination, R2. Case studies of beta-galactosidase, glucoamylase, lipase, and xylanase production processes will be used as examples.

Algorithms↗
PubMed2001

Application of neural networks to the classification of pancreatic intraductal proliferative lesions.

The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and other tumours requiring pancreatic resection. Intraductal lesions were classified as low and high grade as previously described. The image analysis system consisted of a microscope, CCD camera combined with a PC and AnalySIS v. 2.11 software. The following texture characteristics were measured: variance of grey levels, features extracted from the grey levels correlation matrix and mean values, variance and standard deviation of the energy obtained from Laws matrices. Furthermore we used moments derived invariants and basic geometric data such as surface area, the minimum and maximum diameter and shape factor. The sets of data were randomly divided into training and testing groups. The training of the network using the back-propagation algorithm, and the final classification of data was carried out with a neural network simulator SNNS v. 4.1. We studied the efficacy of networks containing from one to three hidden layers. Using the best network, containing three hidden layers, the rate of correct classification of nuclei was 73%, and the rate of misdiagnosis was 3%; in 24% the network response was ambiguous. The present findings may serve as a starting point in search for methods facilitating early diagnosis of ductal pancreatic carcinoma.

Adenocarcinoma↗
PubMed2005

High-speed face recognition based on discrete cosine transform and RBF neural networks.

In this paper, an efficient method for high-speed face recognition based on the discrete cosine transform (DCT), the Fisher's linear discriminant (FLD) and radial basis function (RBF) neural networks is presented. First, the dimensionality of the original face image is reduced by using the DCT and the large area illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. Next, the truncated DCT coefficient vectors are clustered using the proposed clustering algorithm. This process makes the subsequent FLD more efficient. After implementing the FLD, the most discriminating and invariant facial features are maintained and the training samples are clustered well. As a consequence, further parameter estimation for the RBF neural networks is fulfilled easily which facilitates fast training in the RBF neural networks. Simulation results show that the proposed system achieves excellent performance with high training and recognition speed, high recognition rate as well as very good illumination robustness.

Algorithms↗
PubMed2004

Improving option pricing with the product constrained hybrid neural network.

In the past decade, many studies across various financial markets have shown conventional option pricing models to be inaccurate. To improve their accuracy, various researchers have turned to artificial neural networks (ANNs). In this work a neural network is constrained in such a way that pricing must be rational at the option-pricing boundaries. The constraints serve to change the regression surface of the ANN so that option pricing accuracy is improved in the locale of the boundaries. These constraints lead to statistically and economically significant out-performance, relative to both the most accurate conventional and nonconventional option pricing models.

Models, Economic↗
PubMed2006

Analog neural network for support vector machine learning.

An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems.

Computers, Analog↗
PubMed2003

Detection of lameness and determination of the affected forelimb in horses by use of continuous wavelet transformation and neural network classification of kinematic data.

OBJECTIVE: To investigate continuous wavelet transformation and neural network classification of gait data for detecting forelimb lameness in horses. ANIMALS: 12 adult horses with mild forelimb lameness. PROCEDURE: Position of the head and right forelimb foot, metacarpophalangeal (ie, fetlock), carpal, and elbow joints was determined by use of kinematic analysis before and after palmar digital nerve blocks. We obtained 8 recordings from horses without lameness, 8 with right forelimb lameness, and 8 with left forelimb lameness. Vertical and horizontal position of the head and vertical position of the foot, fetlock, carpal, and elbow joints were processed by continuous wavelet transformation. Feature vectors were created from the transformed signals and a neural network trained with data from 6 horses, which was then tested on the remaining 2 horses for each category until each horse was used twice for training and testing. Correct classification percentage (CCP) was calculated for each combination of gait signals tested. RESULTS: Wavelet-transformed vertical position of the head and right forelimb foot had greater CCP (85%) than untransformed data (21%). Adding data from the fetlock, carpal, or elbow joints did not improve CCP over that for the head and foot alone. CONCLUSIONS AND CLINICAL RELEVANCE: Wavelet transformation of gait data extracts information that is important for the detection and differentiation of forelimb lameness of horses. All of the necessary information to detect lameness and differentiate the side of lameness can be obtained by observation of vertical head movement in concert with movement of the foot of 1 forelimb.

Animals↗
PubMed2004

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water.

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

Automation↗
PubMed2003

Artificial neural networks for species identification by taxonomists.

This paper is a study of the value of applying artificial neural networks (ANNs), specifically a multilayer perceptron (MLP), to identification of higher plants using morphological characters collected by conventional means. A practical methodology is thus demonstrated to enable botanical or zoological taxonomists to use ANNs as advisory tools for identification purposes. A comparison is made between the ability of the neural network and that of traditional methods for plant identification by means of a case study in the flowering plant genus Lithops N.E. Brown (Aizoaceae). In particular, a comparison is made with taxonomic keys generated by means of the DELTA system. The ANN is found to perform better than the DELTA key generator, for conditions where the available data is limited, and species relatively difficult to distinguish.

Aizoaceae↗
PubMed2000

Neural network methods for identification and optimization of quantum mechanical features needed for bioactivity.

This paper presents a new approach to the discovery and design of bioactive compounds. The focus of this application will be on the analysis of enzymatic inhibitors. At present the discovery of enzymatic inhibitors for therapeutic use is often accomplished through random searches. The first phase of discovery is a random search through a large pre-fabricated chemical library. Many molecules are tested with refined enzyme for signs of inhibition. Once a group of lead compounds have been discovered the chemical intuition of biochemists is used to find structurally related compounds that are more effective. This step requires new molecules to be conceived and synthesized, and it is the most time-consuming and expensive step. The development of computational and theoretical methods for prediction of the molecular structure that would bind most tightly prior to synthesis and testing, would facilitate the design of novel inhibitors. In the past, our work has focused on solving the problem of predicting the bioactivity of a molecule prior to synthesis. We used a neural network trained with the bioactivity of known compounds to predict the bioactivity of unknown compounds. In our current work, we use a separate neural network in conjunction with a trained neural network in an attempt to gain insight as to how to modify existing compounds and increase their bioactivity.

Enzyme Inhibitors↗
PubMed1982

Dynamic connections in neural networks.

Massively parallel (neural-like) networks are receiving increasing attention as a mechanism for expressing information processing models. By exploiting powerful primitive units and stability-preserving construction rules, various workers have been able to construct and test quite complex models, particularly in vision research. But all of the detailed technical work was concerned with the structure and behavior of fixed networks. The purpose of this paper is to extend the methodology to cover several aspects of change and memory.

Memory↗
PubMed2005

On the global output convergence of a class of recurrent neural networks with time-varying inputs.

This paper studies the global output convergence of a class of recurrent neural networks with globally Lipschitz continuous and monotone nondecreasing activation functions and locally Lipschitz continuous time-varying inputs. We establish two sufficient conditions for global output convergence of this class of neural networks. Symmetry in the connection weight matrix is not required in the present results which extend the existing ones.

Algorithms↗
PubMed1999

DNA ploidy and cell cycle distribution of breast cancer aspirate cells measured by image cytometry and analyzed by artificial neural networks for their prognostic significance.

Chromosomal abnormalities are commonly associated with cancer, and their importance in the pathogenesis of the disease has been well recognized. Also recognized in recent years is the possibility that, together with chromosomal abnormalities, DNA ploidy of breast cancer aspirate cells, measured by image cytometric techniques, may correlate with prognosis of the disease. Here, we have examined the use of an artificial neural network to predict: 1) subclinical metastatic disease in the regional lymph nodes and 2) histological assessment, through the analysis of data obtained by image cytometric techniques of fine needle aspirates of breast tumors. The cellular features considered were: 1) DNA ploidy measured in terms of nuclear DNA content as well as by cell cycle distribution; 2) size of the S-phase fraction; and 3) nuclear pleomorphism. A further objective of the study was to analyze individual markers in terms of impact significance on predicting outcome in both cases. DNA ploidy, indicated by cell cycle distribution, was found markedly to influence the prediction of nodal spread of breast cancer, and nuclear pleomorphism to a lesser degree. Furthermore, a comparison between histological assessment and artificial neural network prediction shows a closer correlation between the neural approach and the development of further metastases as indicated in subsequent follow-up, than does histological assessment. These data demonstrate that artificial neural networks are capable of providing powerful and reliable indicators of possible lymph node metastasis, using measurements of cellular features alone.

Breast Neoplasms↗
PubMed1997

Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma.

BACKGROUND: Neural networks have been used to predict outcome in cancer patients. Their accuracy compared with standard statistical methods has not been fully assessed. METHODS: In this study, the authors examined the ability to predict the outcome of surgery in 620 patients with nonsmall cell lung carcinoma (NSCLC) by a genetic algorithm neural network (GANN) using Bayes' theorem compared with logistic regression, and the predictive value of tumor volume measures in addition to standard indices such as histologic type and stage. Predictive methods were compared by examining accuracy of classifying target outcome of patients living or dead at 6, 12, 18, and 24 months after surgery. RESULTS: GANN was a significantly better predictor of outcome than logistic regression at all time points (McNemar, P < 0.01). Measures of tumor volume produced significant improvement in the prediction of 12-, 18-, and 24-month time points with GANN, and at 18- and 24-month time points with logistic regression (Wilcoxon matched pairs signed rank test, P < 0.02). CONCLUSIONS: In this study of surgically treated NSCLC patients, outcome predictions were significantly improved by including measures of tumor volume. For predicting individual patient outcome, GANN was found to be highly accurate and significantly better than logistic regression.

Aged↗