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PubMed2004

Forecasting model for the incidence of hepatitis A based on artificial neural network.

AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon. METHODS: The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis to determine whether there was any autoregression phenomenon in the data. Then the data of the incidence were switched into [0,1] intervals as the network theoretical output. The data from 1981 to 1997 were used as the training and verifying sets and the data from 1998 to 2001 were made up into the test set. STATISTICA neural network (ST NN) was used to construct, train and simulate the artificial neural network. RESULTS: Twenty-four networks were tested and seven were retained. The best network we found had excellent performance, its regression ratio was 0.73, and its correlation was 0.69. There were 2 input variables in the network, one was AR(1), and the other was time. The number of units in hidden layer was 3. In ARIMA time series analysis results, the best model was first order autoregression without difference and smoothness. The total sum square error of the ANN model was 9 090.21, the sum square error of the training set and testing set was 8 377.52 and 712.69, respectively, they were all less than that of ARIMA model. The corresponding value of ARIMA was 12 291.79, 8 944.95 and 3 346.84, respectively. The correlation coefficient of nonlinear regression (R(NL)) of ANN was 0.71, while the R(NL) of ARIMA linear autoregression model was 0.66. CONCLUSION: ANN is superior to conventional methods in forecasting the incidence of hepatitis A which has an autoregression phenomenon.

China↗
PubMed1997

Data modelling with neural networks: advantages and limitations.

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.

Data Interpretation, Statistical↗
PubMed2004

Orthogonality of decision boundaries in complex-valued neural networks.

This letter presents some results of an analysis on the decision boundaries of complex-valued neural networks whose weights, threshold values, input and output signals are all complex numbers. The main results may be summarized as follows. (1) A decision boundary of a single complex-valued neuron consists of two hypersurfaces that intersect orthogonally, and divides a decision region into four equal sections. The XOR problem and the detection of symmetry problem that cannot be solved with two-layered real-valued neural networks, can be solved by two-layered complex-valued neural networks with the orthogonal decision boundaries, which reveals a potent computational power of complex-valued neural nets. Furthermore, the fading equalization problem can be successfully solved by the two-layered complex-valued neural network with the highest generalization ability. (2) A decision boundary of a three-layered complex-valued neural network has the orthogonal property as a basic structure, and its two hypersurfaces approach orthogonality as all the net inputs to each hidden neuron grow. In particular, most of the decision boundaries in the three-layered complex-valued neural network inetersect orthogonally when the network is trained using Complex-BP algorithm. As a result, the orthogonality of the decision boundaries improves its generalization ability. (3) The average of the learning speed of the Complex-BP is several times faster than that of the Real-BP. The standard deviation of the learning speed of the Complex-BP is smaller than that of the Real-BP. It seems that the complex-valued neural network and the related algorithm are natural for learning complex-valued patterns for the above reasons.

Algorithms↗
PubMed2006

Neural networks for continuous online learning and control.

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Algorithms↗
PubMed1997

Neural network modeling for estimation of the aqueous solubility of structurally related drugs.

The ability of neural network models to predict aqueous solubility within series of structurally related drugs was evaluated. Three sets of compounds representing different drug classes (28 steroids, 31 barbituric acid derivatives, and 24 heterocyclic reverse transcriptase inhibitors) were studied. Topological descriptors (connectivity indices, kappa indices, and electrotopological state indices) were used to link the structures of compounds with their aqueous solubility. Separate models were built for each class of drugs using back-propagation neural networks with one hidden layer and five topological indices as input parameters. The effect of network size and training time on the prediction ability of the network was studied by the leave-one-out (LOO) procedure. In all three compound groups a neural network structure of 5-3-1 was optimal. To avoid chance effects, artificial neural network (ANN) ensembles (i.e.; averaging neural network predictions over several independent networks) were used. The cross-validated squared correlation coefficient (Q2) for 10 averaged predictions was 0.796 in the case of the steroid set. The corresponding standard error of prediction (SDEP) was 0.288 log units. For the barbiturates, Q2 and SDEP were 0.856 and 0.383, respectively, and for the RT inhibitors, these parameters were 0.721 and 0.401, respectively. The results indicate that neural networks can produce useful models of the aqueous solubility of a congeneric set of compounds, even with simple structural parameters.

Drug Design↗
PubMed1999

Application of two neural network paradigms to the study of voluntary employee turnover.

Two neural network paradigms--multilayer perceptron and learning vector quantization--were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.

Adult↗
PubMed2001

Simultaneous determination of composition of human urinary calculi by use of artificial neural networks.

A new chemometric method, which uses artificial neural networks (ANN), is presented for determination of the composition of urinary calculi. The selected constituents were whewellite, weddellite, and uric acid from which approximately 40% of the urinary calculi obtained from Macedonia patients are composed. The results for the synthetic mixtures were better then those obtained by partial least squares (PLS) regression or by the principal component regression (PCR), because neural networks have better prediction capacity. The generalization abilities of the optimized neural networks were checked using the standard addition method on carefully selected real natural samples.

Humans↗
PubMed1993

Methodological and theoretical issues in neural network models of frontal cognitive functions.

Neural network models have made significant strides in recent years toward modeling of neuropsychological data. In particular, three different research groups, including that of the present authors, have simulated in model networks some of the behavioral effects of frontal lobe damage. In this article we review these models and discuss their significance in terms of hierarchical organization in the nervous system. These models, to varying degrees, incorporate several widely used neural network principles that have also been used to model a wide range of data in other areas such as categorization, conditioning, and motor control. These principles include associative learning, competition, opponent processing, neuromodulation, and interlevel resonant feedback. Specifically, we show how combinations of these principles serve to model the attentional requirements of cognitive tasks and motor plans.

Animals↗
PubMed1996

Adult rat vigilance states discrimination by artificial neural networks using a single EEG channel.

Two multilayer neural networks were designed to discriminate vigilance states (waking, paradoxical sleep, and non-REM sleep) in the rat using a single parieto-occipital EEG derivation. After filtering (bandwidth 3.18-25 Hz) and digitization at 512 HZ, the EEG signal was segmented into eight second epochs. Five variables (three statistical, two temporal) were extracted from each epoch. The first network computed an epoch by epoch classification, while the second network also utilized contextual information from contiguous epochs. A specific postprocessing procedure was developed to enhance the vigilance state discrimination of the neural networks designed and especially paradoxical sleep state estimation. The classifications made by the networks (with or without the postprocessing procedure) for six rats were compared to these made by two human experts using EMG and EEG informations on 63,000 epochs. High rates of agreement (> 90%) between humans and neural networks classifications were obtained. In view of its development possibilities and its applicability to other signals, this method could prove of value in biomedical research.

Animals↗
PubMed2002

A preliminary evaluation of neural network analysis for pharmacodynamic modeling of the dosing of the hydroxymethylglutaryl coenzyme A-reductase inhibitors simvastatin and atorvastatin.

BACKGROUND: Neural networks have been used in diagnosing and treating many diseases, including the diagnosis of myocardial infarction and insulin dosing in diabetes mellitus. OBJECTIVE: The goal of this study was to develop a preliminary pharmacodynamic model for dosing of the hydroxymethylglutaryl coenzyme A (HMG-CoA)-reductase inhibitors simvastatin and atorvastatin using neural network analysis. METHODS: Using NeuralSIM neural network software (NeuralWare, Carnegie, Pa), lipid panels from patients at a hospital lipid clinic were entered as inputs for the model, and the doses of simvastatin or atorvastatin that achieved those lipid results were used as outputs. The network was trained using 2 different data sets and was run using a subset of the data as inputs. The results of the neural network run (predictions) were compared with the actual doses used as directed by the hospital pharmacist in accordance with the lipid clinic protocol. RESULTS: Complete data sets were available for 17 patients (11 men, 6 women). The mean age of these patients was 56.7 years (range, 40-67 years). The neural network model based on data set 1 predicted a dose that was within 95% of the actual dose 7 of 12 times and predicted use of the drug actually used 13 of 19 (68.4%) times. The neural network based on data set 2 predicted use of the drug actually used 10 of 17 (58.8%) times, but the predicted doses never approximated the actual doses by > or = 90%. CONCLUSIONS: A neural network model for the dosing of the HMG-CoA-reductase inhibitors simvastatin and atorvastatin demonstrated an ability to predict appropriate dosing, but inclusion of other factors (eg, age, body weight, sex) and a larger sample size may be necessary for development of a more accurate model.

Adult↗
PubMed2005

Improvement of trypanocidal metabolites production by Aspergillus fumigatus using neural networks.

An optimization procedure using artificial neural networks was developed to determine the optimal combination of parameters, such as medium culture, initial pH, temperature and time of fermentation for maximal trypanocidal metabolites production by Aspergillus fumigatus. A data set of 81 experiments was carried out and an artificial neural network was trained to identify the optimal conditions for this process. Good correlation was obtained between the experimental and predicted values of lysis of the trypomastigote forms of Trypanosoma cruzi (r2 = 0.9990). The simulations of fermentation performance were undertaken on combinations of input variables and the highest level of activity against T. cruzi was obtained from the chloroform extract of the modified Jackson medium culture, initial pH of 6.0, incubated at 40 degrees C for 144 h. It displayed lysis of 95% of the trypomastigote forms of T. cruzi and the red blood cells remained normal.

Animals↗
PubMed1996

What can artificial neural networks teach us about neurodegenerative disorders with extrapyramidal features?

Artificial neural networks (ANNs), computer paradigms that can learn, excel in pattern recognition tasks such as disease diagnosis. Artificial neural networks operate in two different learning modes: supervised, in which a known diagnostic outcome is presented to the ANN, and unsupervised, in which the diagnostic outcome is not presented. A supervised learning ANN could emulate human expert diagnostic performance and identify relevant predictive markers in the diagnostic task, while an unsupervised learning ANN could suggest reasonable alternative diagnostic classification criteria. In the present study, we used ANN methodology to try to overcome the neuropathological difficulties in differentiating the subtypes of progressive supranuclear palsy (PSP), and in differentiating PSP from postencephalitic parkinsonism (PEP) and corticobasal degeneration, or Pick's disease from corticobasal degeneration. First, we applied supervised learning ANN to classify 62 cases of these disorders and to identify diagnostic markers that distinguish them. In a second experiment, we used unsupervised learning ANN to investigate possible alternative nosological classifications. Artificial neural networks input data for each case consisted of values representing histological features, including neurofibrillary tangles, neuronal loss and gliosis found in multiple brain sampling areas. The supervised learning ANN achieved excellent accuracy in classifying PSP but had difficulty classifying the other disorders. This method identified a few features that might help to differentiate PEP, supported currently proposed criteria for Pick's disease, corticobasal degeneration and typical PSP, but detected no features to characterize the atypical subtype of PSP. In general, unsupervised learning ANN supported the present nosological classification for PSP, PEP, Pick's disease and corticobasal degeneration, although it overlapped some groups. Artificial neural networks methodology appears promising for studying neurodegenerative disorders.

Basal Ganglia Diseases↗
PubMed2006

New necessary and sufficient conditions for absolute stability of neural networks.

This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neural networks. The main result is based on a solvable Lie algebra condition, which generalizes existing results for symmetric and normal neural networks. An exponential convergence estimate of the neural networks is also obtained. Further, it is demonstrated how to generate larger sets of weight matrices for absolute stability of the neural networks from known normal weight matrices through simple procedures. The approach is nontrivial in the sense that non-normal matrices can possibly be contained in the resulting weight matrix set. And the results also provide finite checking for robust stability of neural networks in the presence of parameter uncertainties.

Algorithms↗
PubMed1998

Neural network mosaic model for pupillary responses to spatial stimuli.

A neural network mosaic model was developed to investigate the spatial-temporal properties of the human pupillary control system. It was based on the double-layer neural network model developed by Cannon and Robinson and the pupillary dual-path model developed by Sun and Stark. The neural network portion of the model received its input from a sensor array and consisted of a retina-like two-dimensional neuronal layer. The dual-path portion of the model was composed of interconnections of the neurons that formed a mosaic of AC transient and DC sustained paths. The spatial aggregates of the AC and DC signals were input to the AC and DC summing neurons, respectively. Finally, the weighted sum of the aggregate AC and DC signals provided the output for driving the pupillary response. An important property of the model was that it could adaptively learn from training samples by adjustment of the weights. The neural network mosaic model showed excellent performance in simulating both the traditional pupillary phenomena and the new spatial stimulation findings such as responses to change in stimulus pattern and shift of light spot. Moreover, the model could also be used for the diagnosis of clinical deficits and image processing in machine vision.

Brain Stem↗
PubMed2000

Prediction of bladder outlet obstruction in men with lower urinary tract symptoms using artificial neural networks.

PURPOSE: To evaluate the performance of a backpropagation artificial neural network (ANN) in the diagnosis of men with lower urinary tract symptoms (LUTS) and to compare its performance to that of a traditional linear regression model. MATERIALS AND METHODS: 1903 LUTS patients referred to the University Hospital Nijmegen between 1992 and 1998 received routine investigation, consisting of transrectal ultrasonography of the prostate, serum PSA measurement, assessment of symptoms and quality of life by the International Prostate Symptom Score (IPSS), urinary flowmetry with determination of maximum flow rate (Qmax), voided volume and post-void residual urine and full pressure flow studies (PFS). Using a three-layered backpropagation ANN with three hidden nodes, the outcome of PFS, quantified by the Abrams-Griffiths number (AG-number), was estimated based on all available non-invasive diagnostic test results plus patient age. The performance of the network was quantified using sensitivity, specificity and the area under the ROC-curve (AUC). The results of the neural network approach were compared to those of a linear regression analysis. RESULTS: Prostate volume, Qmax, voided volume and post void residual urine showed substantial predictive value concerning the outcome of PFS. Patient age, PSA-level, IPSS and Quality of life did not add to that prediction. Using a cut-off value in predicted and true AG-numbers of 40 cm. H2O, the neural network approach yielded sensitivity and specificity of 71% and 69%, respectively. The AUC of the network was 0.75 (standard error = 0.01). A linear regression model produced identical results. CONCLUSIONS: This study shows that at an individual level, the outcome of PFS cannot be predicted accurately by the available non-invasive tests. The use of ANNs, which are better able than traditional regression models to identify non-linear relations and complex interactions between variables, did not improve the prediction of BOO. Thus, if precise urodynamic information is considered important in the diagnosis of men with LUTS, PFS must be carried out. Both neural networks and regression analysis appear promising to identify patients who should undergo PFS, and those in whom PFS can safely be omitted. Furthermore, the ability of ANNs and regression models to predict treatment result should be evaluated.

Humans↗
PubMed1995

One-year mortality prognosis in heart failure: a neural network approach based on echocardiographic data.

OBJECTIVES: This study sought to assess the usefulness and accuracy of artificial neural networks in the prognosis of 1-year mortality in patients with heart failure. BACKGROUND: Artificial neural networks is a computational technique used to represent and process information by means of networks of interconnected processing elements, similar to neurons. They have found applications in medical decision support systems, particularly in prognosis. METHODS: Clinical and Doppler-derived echocardiographic data from 95 consecutive patients with diffuse impairment of myocardial contractility were studied. After 1 year, data regarding survival or death were obtained and produced the prognostic variable. The data base was divided randomly into a training data set (47 cases, 8 deaths) and a testing data set (48 cases, 7 deaths). Results of artificial neural network classification were compared with those from linear discriminant analysis, clinical judgment and conventional heuristically based programs. RESULTS: The study group included 57 male (47 survivors) and 38 female patients (33 survivors). Linear discriminant analysis was not efficient for separating survivors from nonsurvivors because the accuracy at the ideal cutoff value was only 67.4%, with a sensitivity of 67.5%, positive predictive value of 27.8% and negative predictive value of 91.5%. In contrast, all artificial neural networks were able to predict outcome with an accuracy of 90%, specificity of 93% and sensitivity of 71.4%, for the best artificial neural network. Both clinical judgment and automatic heuristic methods were also inferior in performance. CONCLUSIONS: The artificial neural network method has proved to be reliable for implementing quantitative prognosis of mortality in patients with heart failure. Additional studies with larger numbers of patients are required to better assess the usefulness of artificial neural networks.

Adolescent↗
PubMed2003

Medical diagnosis with C4.5 Rule preceded by artificial neural network ensemble.

Comprehensibility is very important for any machine learning technique to be used in computer-aided medical diagnosis. Since an artificial neural network ensemble is composed of multiple artificial neural networks, its comprehensibility is worse than that of a single artificial neural network. In this paper, C4.5 Rule-PANE which combines artificial neural network ensemble with rule induction by regarding the former as a preprocess of the latter, is proposed. At first, an artificial neural network ensemble is trained. Then, a new training data set is generated by feeding the feature vectors of the original training instances to the trained ensemble and replacing the expected class labels of the original training instances with the class labels output from the ensemble. Additional training data may also be appended by randomly generating feature vectors and combining them with their corresponding class labels output from the ensemble. Finally, a specific rule induction approach, i.e., C4.5 Rule, is used to learn rules from the new training data set. Case studies on diabetes, hepatitis, and breast cancer show that C4.5 Rule-PANE could generate rules with strong generalization ability, which profits from artificial neural network ensemble, and strong comprehensibility, which profits from rule induction.

Diagnosis↗
PubMed1999

Artificial neural networks applied to survival prediction in breast cancer.

In this study, we evaluated the accuracy of a neural network in predicting 5-, 10- and 15-year breast-cancer-specific survival. A series of 951 breast cancer patients was divided into a training set of 651 and a validation set of 300 patients. Eight variables were entered as input to the network: tumor size, axillary nodal status, histological type, mitotic count, nuclear pleomorphism, tubule formation, tumor necrosis and age. The area under the ROC curve (AUC) was used as a measure of accuracy of the prediction models in generating survival estimates for the patients in the independent validation set. The AUC values of the neural network models for 5-, 10- and 15-year breast-cancer-specific survival were 0.909, 0.886 and 0.883, respectively. The corresponding AUC values for logistic regression were 0.897, 0.862 and 0.858. Axillary lymph node status (N0 vs. N+) predicted 5-year survival with a specificity of 71% and a sensitivity of 77%. The sensitivity of the neural network model was 91% at this specificity level. The rate of false predictions at 5 years was 82/300 for nodal status and 40/300 for the neural network. When nodal status was excluded from the neural network model, the rate of false predictions increased only to 49/300 (AUC 0. 877). An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction. The consistently high accuracy over time and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction.

Adult↗