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At least 919 records · Page 51Linked to original sources
PubMed1999

Dynamic monitoring and control of patient anaesthetic and dose levels: time-delay, moving-average neural networks, and principal components analysis.

The goal of this study was to examine the capabilities of neural network models for dynamic monitoring and control of patient anaesthetic and dose levels. The network models that we considered are split into two basic groups: static networks and dynamic networks. Static networks are characterised by equations that are memoryless. On the other hand, dynamic networks are systems with memory. Additionally, principal components analysis was used to introduce a further improvement to network design by reducing the dimensionality of the encoded temporal information. Principal components analysis was applied as both pre-processing and post-processing techniques. In the first instance it was used to reduce the dimensionality of the data to more manageable intrinsic information. In the second instance it was employed to understand how the hidden layers separate the data, in order to optimise the network architecture.

Anesthesia↗
PubMed2003

Inhibitory cotransmission or after-hyperpolarizing potentials can regulate firing in recurrent networks with excitatory metabotropic transmission.

Recurrent networks of neurons communicating via excitatory connections are common in the nervous system. In the absence of mechanisms to control firing (collectively termed negative feedback), these networks are likely to be bistable and unable to meaningfully encode input signals. In most recurrent circuits, negative feedback is provided by a specialized subpopulation of interneurons, but such neurons are absent from some systems, which therefore require other forms of negative feedback. One such circuit is found within the enteric nervous system of the intestine, where AH/Dogiel type II neurons are interconnected via excitatory synapses acting through metabotropic receptors to produce slow excitatory postsynaptic potentials (slow EPSPs). Negative feedback in this recurrent network may come from either inhibitory postsynaptic potentials arising from the terminals that produce slow EPSPs or from the after hyperpolarizing potentials (AHPs) characteristic of these neurons. We have examined these possibilities using mathematical analysis, based on the Wilson-Cowan model, and computer simulations. Analysis of steady states showed that, under appropriate conditions, both types of negative feedback can provide robust regulation of firing allowing the networks to encode input signals. Numerical simulations were performed using large, anatomically realistic networks with realistic models for metabotropic transmission and suppression of the AHP. In the presence of constant exogenous input, parameters controlling aspects of synaptic events were varied, confirming the analytical results for static stimuli. The simulated networks also responded to time varying inputs in a manner consistent with known physiology. In addition, simulation revealed that neurons in networks with inhibitory contransmission fired in erratic bursts, a phenomenon observed in neurons in unparalysed tissue. Thus, either inhibitory contransmission or AHPs, or both, can allow recurrent networks of AH/Dogiel type II neurons to encode ongoing inputs in a biologically useful way. These neurons appear to be intrinsic primary afferent neurons (IPANs), which implies that the IPANs in a region act in a coordinated fashion.

Action Potentials↗
PubMed1996

Agreement between artificial neural networks and experienced electrocardiographer on electrocardiographic diagnosis of healed myocardial infarction.

OBJECTIVES: The purpose of this study was to compare the diagnoses of healed myocardial infarction made from the 12-lead electrocardiogram (ECG) by artificial neural networks and an experienced electrocardiographer. BACKGROUND: Artificial neural networks have proved of value in pattern recognition tasks. Studies of their utility in ECG interpretation have shown performance exceeding that of conventional ECG interpretation programs. The latter present verbal statements, often with an indication of the likelihood for a certain diagnosis, such as "possible left ventricular hypertrophy." A neural network presents its output as a numeric value between 0 and 1; however, these values can be interpreted as Bayesian probabilities. METHODS: The study was based on 351 healthy volunteers and 1,313 patients with a history of chest pain who had undergone diagnostic cardiac catheterization. A 12-lead ECG was recorded in each subject. An expert electrocardiographer classified the ECGs in five different groups by estimating the probability of anterior myocardial infarction. Artificial neural networks were trained and tested to diagnose anterior myocardial infarction. The network outputs were divided into five groups by using the output values and four thresholds between 0 and 1. RESULTS: The neural networks diagnosed healed anterior myocardial infarctions at high levels of sensitivity and specificity. The network outputs were transformed to verbal statements, and the agreement between these probability estimates and those of an expert electrocardiographer was high. CONCLUSIONS: Artificial neural networks can be of value in automated interpretation of ECGs in the near future.

Electrocardiography↗
PubMed2003

On learning to estimate the block directional image of a fingerprint using a hierarchical neural network.

This paper presents a hierarchical neural network architecture for computing fingerprints block directional images. Two separately trained neural networks are connected in series. First, the fingerprint image is divided into 16x16 blocks, each block is submitted to the first network which is a back propagation neural network. It has four counters in its output layer one for each direction to count the main directional codes in each fingerprint block. The output of this network is considered the feature vector for the fingerprint block, which is then submitted to the second network. The second network is a self-organized feature maps neural network uses an unsupervised learning strategy to group the fingerprint blocks into distinct directional classes. In this scheme, there is more than one sub-class for each directional class, an agglomerative hierarchical cluster algorithm for merging two clusters is used to merge two classes if their corresponding distances are below a specified threshold. Results obtained with a real world data set indicate the effectiveness of the proposed architecture.

Algorithms↗
PubMedDate not supplied

Application of four-layer neural network on information extraction.

This paper applies neural network to extract marsh information. An adaptive back-propagation algorithm based on a robust error function is introduced to build a four-layer neural network, and it is used to classify Thematic Mapper (TM) image of Zhalong Wetland in China and then extract marsh information. Comparing marsh information extraction results of the four-layer neural network with three-layer neural network and the maximum likelihood classifier, conclusion can be drawn as follows: the structure of the four-layer neural network and the adaptive back-propagation algorithm based on the robust error function is effective to extract marsh information. The four-layer neural network adopted in this paper succeeded in building the complex model of TM image, and it avoided the problem of great storage of remotely sensed data, and the adaptive back-propagation algorithm speeded up the descending of error. Above all, the four-layer neural network is superior to the three-layer neural network and the maximum likelihood classifier in the accuracy of the total classification and marsh information extraction.

Algorithms↗
PubMed2004

Self-organising continuous attractor networks with multiple activity packets, and the representation of space.

'Continuous attractor' neural networks can maintain a localised packet of neuronal activity representing the current state of an agent in a continuous space without external sensory input. In applications such as the representation of head direction or location in the environment, only one packet of activity is needed. For some spatial computations a number of different locations, each with its own features, must be held in memory. We extend previous approaches to continuous attractor networks (in which one packet of activity is maintained active) by showing that a single continuous attractor network can maintain multiple packets of activity simultaneously, if each packet is in a different state space or map. We also show how such a network could by learning self-organise to enable the packets in each space to be moved continuously in that space by idiothetic (motion) inputs. We show how such multi-packet continuous attractor networks could be used to maintain different types of feature (such as form vs colour) simultaneously active in the correct location in a spatial representation. We also show how high-order synapses can improve the performance of these networks, and how the location of a packet could be read by motor networks. The multiple packet continuous attractor networks described here may be used for spatial representations in brain areas such as the parietal cortex and hippocampus.

Brain↗
PubMed2002

Lung cancer cell identification based on artificial neural network ensembles.

An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.

Adenocarcinoma↗
PubMed1999

Using sensitivity analysis for efficient quantification of a belief network.

Sensitivity analysis is a method to investigate the effects of varying a model's parameters on its predictions. It was recently suggested as a suitable means to facilitate quantifying the joint probability distribution of a Bayesian belief network. This article presents practical experience with performing sensitivity analyses on a belief network in the field of medical prognosis and treatment planning. Three network quantifications with different levels of informedness were constructed. Two poorly-informed quantifications were improved by replacing the most influential parameters with the corresponding parameter estimates from the well-informed network quantification; these influential parameters were found by performing one-way sensitivity analyses. Subsequently, the results of the replacements were investigated by comparing network predictions. It was found that it may be sufficient to gather a limited number of highly-informed network parameters to obtain a satisfying network quantification. It is therefore concluded that sensitivity analysis can be used to improve the efficiency of quantifying a belief network.

Algorithms↗
PubMed2002

Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra.

Temperature constrained cascade correlation networks (TCCCNs) are computational neural networks that configure their own architecture, train rapidly, and give reproducible prediction results. TCCCN classification models were built using the Latin-partition method for five classes of pathogenic bacteria. Neural networks are problematic in that the relationships among the inputs (i.e., mass spectra) and the outputs (i.e., the bacterial identities) are not apparent. In this study, neural network models were constructed that successfully classified the targeted bacteria and the classification model was validated using sensitivity and target transformation factor analysis (TTFA). Without validation of the classification model, it is impossible to ascertain whether the bacteria are classified by peaks in the mass spectrum that have no causal relationships with the bacteria, but instead randomly correlate with the bacterial classes. Multiple single output network models did not offer any benefits when compared to single network models that had multiple outputs. A multiple output TCCCN model achieved classification accuracies of 96 +/- 2% and exhibited improved performance over multiple single output TCCCN models. Chemical ionization mass spectra were obtained from in situ thermal hydrolysis methylation of freeze-dried bacteria. Mass spectral peaks that pertain to the neural network classification model of the pathogenic bacterial classes were obtained by sensitivity analysis. A significant number of mass spectral peaks that had high sensitivity corresponded to known biomarkers, which is the first time that the significant peaks used by a neural network model to classify mass spectra have been divulged. Furthermore, TTFA furnishes a useful visual target as to which peaks in the mass spectrum correlate with the bacterial identities.

Bacteria↗
PubMed2004

Implanting telehealth network for paediatric cardiology: learning from the Quebec experience.

The implementation committee of the Quebec Child Telehealth Network was formed in 1997, with a mandate to build a network dedicated to the diagnosis of congenital cardiac disease via telemedicine. We devised criterions for selection to determine which peripheral centres would be linked by telemedicine to the university-based services for paediatric cardiology provided in the Canadian Province of Quebec. The criterions included: distance from a university centre, number of births per year, and presence of an already-established outreach clinic for paediatric cardiology. The Quebec Network became operational in 2000, and was composed of 32 peripheral centres and 4 university centres. A total of 363 transmissions of echocardiograms occurred over a 3-year period from January 2000 to December 2002. Peripheral centres located at a distance greater than 100 kilometres from a university centre were 8.5 times more likely to use the network. Criterions other than distance did not influence whether or not a peripheral centre used the network. Cardiac abnormalities were identified in almost two-thirds of the transmissions. The use of the Quebec Network resulted in the avoidance of transfers or clinic visits to university hospitals in seven-tenths of cases. We conclude that distance greater than 100 kilometres from a centre offering subspecialty services in paediatric cardiology is the most important criterion for choosing the peripheral centres that are most likely to use a telehealth network. In its first three years of operation, the telehealth network had a major impact on the delivery of paediatric cardiac care, improving access to subspecialty services across the province.

Computer Communication Networks↗
PubMed1999

Application of multilayer feed-forward neural networks to automated compound identification in low-resolution open-path FT-IR spectrometry.

A drawback of current open-path Fourier transform infrared (OP/FT-IR) systems is that they need a human expert to determine those compounds that may be quantified from a given spectrum. In this work, multilayer feed-forward neural networks with one hidden layer were used to automatically recognize compounds in an OP/FT-IR spectrum without compensation of absorption lines due to atmospheric H2O and CO2. The networks were trained by fast-back-propagation. The training set comprised spectra that were synthesized by digitally adding randomly scaled reference spectra to actual open-path background spectra measured over a variety of path lengths and temperatures. The reference spectra of 109 compounds were used to synthesize the training spectra. Each neural network was trained to recognize only one compound in the presence of up to 10 other interferences in an OP/FT-IR spectrum. Every compound in a database of vaporphase reference spectra can be encoded in an independent neural network so that a neural network library can be established. When these networks are used for the identification of compounds, the process is analogous to spectral library searching. The effect of learning rate and band intensities on the convergence of network training was examined. The networks were successfully used to recognize five alcohols and two chlorinated compounds in field-measured controlled-release OP/FT-IR spectra of mixtures of these compounds.

Neural Networks, Computer↗
PubMed1997

Application of neural networks to population pharmacokinetic data analysis.

This research examined the applicability of using a neural network approach to analyze population pharmacokinetic data. Such data were collected retrospectively from pediatric patients who had received tobramycin for the treatment of bacterial infection. The information collected included patient-related demographic variables (age, weight, gender, and other underlying illness), the individual's dosing regimens (dose and dosing interval), time of blood drawn, and the resulting tobramycin concentration. Neural networks were trained with this information to capture the relationships between the plasma tobramycin levels and the following factors: patient-related demographic factors, dosing regimens, and time of blood drawn. The data were also analyzed using a standard population pharmacokinetic modeling program, NON-MEM. The observed vs predicted concentration relationships obtained from the neural network approach were similar to those from NONMEM. The residuals of the predictions from neural network analyses showed a positive correlation with that from NONMEM. Average absolute errors were 33.9 and 37.3% for neural networks and 39.9% for NONMEM. Average prediction errors were found to be 2.59 and -5.01% for neural networks and 17.7% for NONMEM. We concluded that neural networks were capable of capturing the relationships between plasma drug levels and patient-related prognostic factors from routinely collected sparse within-patient pharmacokinetic data. Neural networks can therefore be considered to have potential to become a useful analytical tool for population pharmacokinetic data analysis.

Anti-Bacterial Agents↗
PubMedDate not supplied

Modeling of substance P and 5-HT induced synaptic plasticity in the lamprey spinal CPG: consequences for network pattern generation.

Consequences of synaptic plasticity in the lamprey spinal CPG are analyzed by means of simulations. This is motivated by the effects substance P (a tachykinin) and serotonin (5-hydroxytryptamin; 5-HT) have on synaptic transmission in the locomotor network. Activity-dependent synaptic depression and potentiation have recently been shown experimentally using paired intracellular recordings. Although normally activity-dependent plasticity presumably does not contribute to the patterning of network activity, this changes in the presence of the neuromodulators substance P and 5-HT, which evoke significant plasticity. Substance P can induce a faster and larger depression of inhibitory connections but potentiation of excitatory inputs, whereas 5-HT induces facilitation of both inhibitory and excitatory inputs. Changes in the amplitude of the first postsynaptic potential are also seen. These changes could thus be a potential mechanism underlying the modulatory role these substances have on the rhythmic network activity. The aim of the present study has been to implement the activity dependent synaptic depression and facilitation induced by substance P and 5-HT into two alternative models of the lamprey spinal locomotor network, one relying on reciprocal inhibition for bursting and one in which each hemicord is capable of oscillations. The consequences of the plasticity of inhibitory and excitatory connections are then explored on the network level. In the intact spinal cord, tachykinins and 5-HT, which can be endogenously released, increase and decrease the frequency of the alternating left-right burst pattern, respectively. The frequency decreasing effect of 5-HT has previously been explained based on its conductance decreasing effect on K(Ca) underlying the postspike afterhyperpolarization (AHP). The present simulations show that short-term synaptic plasticity may have strong effects on frequency regulation in the lamprey spinal CPG. In the network model relying on reciprocal inhibition, the observed effects substance P and 5-HT have on network behavior (i.e., a frequency increase and decrease respectively) can to a substantial part be explained by their effects on the total extent and time dynamics of synaptic depression and facilitation. The cellular effects of these substances will in the 5-HT case further contribute to its network effect.

Action Potentials↗
PubMed1995

Neural network predicted peak and trough gentamicin concentrations.

Predictions of steady state peak and trough serum gentamicin concentrations were compared between a traditional population kinetic method using the computer program NONMEM to an empirical approach using neural networks. Predictions were made in 111 patients with peak concentrations between 2.5 and 6.0 micrograms/ml using the patient factors age, height, weight, dose, dose interval, body surface area, serum creatinine, and creatinine clearance. Predictions were also made on 33 observations that were outside the 2.5 and 6.0 micrograms/ml range. Neural networks made peak serum concentration predictions within the 2.5-6.0 micrograms/ml range with statistically less bias and comparable precision with paired NONMEM predictions. Trough serum concentration predictions were similar using both neural networks and NONMEM. The prediction error for peak serum concentrations averaged 16.5% for the neural networks and 18.6% for NONMEM. Average prediction errors for serum trough concentrations were 48.3% for neural networks and 59.0% for NONMEM. NONMEM provided numerically more precise and less biased predictions when extrapolating outside the 2.5 and 6.0 micrograms/ml range. The observed peak serum concentration distribution was multimodal and the neural network reproduced this distribution with less difference between the actual distribution and the predicted distribution than NONMEM. It is concluded that neural networks can predict serum drug concentrations of gentamicin. Neural networks may be useful in predicting the clinical pharmacokinetics of drugs.

Adult↗
PubMed1999

Myocardial SPET: artificial neural networks describe extent and severity of perfusion defects.

Artificial neural networks are computer programs that learn from examples. They have been successfully used to detect coronary artery disease from myocardial perfusion images. The purpose of the present study was to develop neural networks that could classify myocardial scintigrams regarding reversibility, localization, severity and extent of perfusion defects. Rest/exercise technetium-99m sestamibi scintigrams from 338 patients were studied. The classifications of two experts were employed as the gold standard. Artificial neural networks were trained to classify both reversible (ischaemia) and non-reversible (infarct) defects in three vascular territories, corresponding to the main coronary arteries. The extent (small or large) and severity (mild or severe) of the defects were described by the networks. After the training process, separate test sets were used to compare the neural networks with one of the experts who reclassified the scintigrams two months later. The neural networks made correct classifications in 71% of the test cases and the human expert in 70% (P=0.10). It was concluded that artificial neural networks can be trained to make clinical interpretations of myocardial perfusion scintigrams. The results indicate that networks can assist physicians in achieving correct interpretations and thereby improve the diagnostic accuracy of medical imaging.

Adult↗
PubMed1997

Neural networks as predictors of outcomes in alcoholic patients with severe liver disease.

We developed and evaluated neural networks as predictors of outcomes in alcoholic patients with severe liver disease using commonly available clinical and laboratory values. Hospital charts of 144 patients were reviewed. Nine variables (five laboratory, four clinical) were recorded along with in-hospital death or survival. Data were organized into separate development and validation sets. Neural network predictions of survival were compared with those of the Maddrey discriminant function and logistic regression models developed on the same data. Model performance was evaluated by comparing areas under receiver-operating characteristic (ROC) curves and the distributions of model scores. Survivors had significantly different laboratory and clinical characteristics, the most important being a higher prothrombin time, lower bilirubin, and lower incidence of encephalopathy. Neural network performance was significantly better than that of the Maddrey score (ROC areas, 81.5% vs. 73.8%; P = .04). The ROC area for neural networks was similar to that of logistic regression (ROC area 78.2%; P = .3), but the neural networks were more successful in classifying patients into low- and high-risk groups (P < .001). A neural network score with laboratory data from hospital-day 7 improved prognostic accuracy further to 84.3%. After adjusting for baseline risk, the neural network change in illness severity was still a significant predictor of mortality (P = .001). Neural networks using clinical and laboratory data showed a high prognostic accuracy for predicting mortality in alcoholic patients with severe liver disease.

Adult↗
PubMed2002

Classification of scale-free networks.

While the emergence of a power-law degree distribution in complex networks is intriguing, the degree exponent is not universal. Here we show that the between ness centrality displays a power-law distribution with an exponent eta, which is robust, and use it to classify the scale-free networks. We have observed two universality classes with eta approximately equal 2.2(1) and 2.0, respectively. Real-world networks for the former are the protein-interaction networks, the metabolic networks for eukaryotes and bacteria, and the coauthorship network, and those for the latter one are the Internet, the World Wide Web, and the metabolic networks for Archaea. Distinct features of the mass-distance relation, generic topology of geodesics, and resilience under attack of the two classes are identified. Various model networks also belong to either of the two classes, while their degree exponents are tunable.

Ascomycota↗
PubMed2001

Neural model of the genetic network.

Many cell control processes consist of networks of interacting elements that affect the state of each other over time. Such an arrangement resembles the principles of artificial neural networks, in which the state of a particular node depends on the combination of the states of other neurons. The lambda bacteriophage lysis/lysogeny decision circuit can be represented by such a network. It is used here as a model for testing the validity of a neural approach to the analysis of genetic networks. The model considers multigenic regulation including positive and negative feedback. It is used to simulate the dynamics of the lambda phage regulatory system; the results are compared with experimental observation. The comparison proves that the neural network model describes behavior of the system in full agreement with experiments; moreover, it predicts its function in experimentally inaccessible situations and explains the experimental observations. The application of the principles of neural networks to the cell control system leads to conclusions about the stability and redundancy of genetic networks and the cell functionality. Reverse engineering of the biochemical pathways from proteomics and DNA micro array data using the suggested neural network model is discussed.

Bacteriophage lambda↗