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At least 1,189 records · Page 66Linked to original sources
PubMed1996

Overview of National Cancer Institute networks for cancer control research in Native American populations.

BACKGROUND: The National Cancer Institute established two Native American cancer control research networks. These networks were created to develop and promote a cadre of cancer prevention and control scientists to conduct culturally sensitive research among Native American populations (i.e., American Indian, Alaska Native, Native Hawaiian, and American Samoan). The primary objective of these research efforts is to reduce morbidity and mortality from cancer. Toward reaching this objective, the networks have (1) conducted research, (2) affiliated with or stimulated other groups to conduct research, or (3) facilitated research by encouraging the training of new researchers of Native American origin to promote prevention and early detection as well as state-of-the-art treatment among their people. METHODS: The networks are organized and financially supported by the NCI. Members of the network have established working committees that follow up on tasks identified during the meetings. RESULTS: Accomplishments of these networks have been recorded between 1990 and 1996. Two strategic plans for cancer control research among American Indians and Alaska Natives have been drafted and disseminated. One of the plans was designed for federal agencies and the other for state health departments. The networks have conducted three national surveys regarding native peoples' access to cancer prevention and control services and conducted two national Native American cancer conferences. The Native Hawaiian/American Samoan Task Force was established in 1992 due to the high incidence and mortality statistics for cancer in these populations. CONCLUSIONS: Through support mechanisms such as those implemented by the NCI, independent networks can be created to advocate actively for cancer prevention and control efforts within Native American communities, among nonnatives, and within professional organizations and governmental agencies.

American Samoa↗
PubMed2026

Network-based integration of metabolomics data from large-scale repositories.

INTRODUCTION: Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility. OBJECTIVES: This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content. METHODS: We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main . RESULTS: As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected. CONCLUSION: Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.

Metabolomics↗
PubMed1997

Automatic detection of glaucomatous visual field progression with neural networks.

OBJECTIVE: To evaluate computerized neural networks to determine visual field progression in patients with glaucoma. METHODS: Two hundred thirty-three series of Octopus G1 visual fields of 181 patients with glaucoma were collected. Each series was composed of 4 or more reliable visual fields from patients who had previously undergone automated perimetry. The visual fields were independently evaluated in a masked fashion by 3 experienced observers (K.N.-M, M.W., and J.C.) and were judged to show progression based on the agreement of 2 observers. The stable and progressed series were matched for mean defect at baseline. The threshold data were submitted to a back propagation neural network that was trained to classify each series as stable or progressed. Two thirds of the data were used for the training and the remaining one third to test the performance of the network. This was repeated 3 times to classify all of the series (changing the training and test series). RESULTS: Fifty-nine series of visual fields showed progression and 151 were judged stable. Neural network sensitivity was 73% and specificity was 88% (threshold for progression = 0.5). The concordance of the neural network with the observers was good (0.50 < or = kappa > or = 0.64). CONCLUSIONS: A neural network can be trained to recognize visual field progression in good concordance with experienced observers. Neural networks may be used to aid the physician in the evaluation of glaucomatous visual field progression.

Aged↗
PubMed2001

Artificial neural networks: opening the black box.

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, especially medicine. For example, neural networks can extract new medical information from raw data, build computer models that are useful for medical decision-making, and aid in the distribution of medical expertise. Because many important neural network applications currently are emerging, the authors have prepared this article to bring a clearer understanding of these biologically inspired computing paradigms to anyone interested in exploring their use in medicine. They discuss the historical development of neural networks and provide the basic operational mathematics for the popular multilayered perceptron. The authors also describe good training, validation, and testing techniques, and discuss measurements of performance and reliability, including the use of bootstrap methods to obtain confidence intervals. Because it is possible to predict outcomes for individual patients with a neural network, the authors discuss the paradigm shift that is taking place from previous "bin-model" approaches, in which patient outcome and management is assumed from the statistical groups in which the patient fits. The authors explain that with neural networks it is possible to mediate predictions for individual patients with prevalence and misclassification cost considerations using receiver operating characteristic methodology. The authors illustrate their findings with examples that include prostate carcinoma detection, coronary heart disease risk prediction, and medication dosing. The authors identify and discuss obstacles to success, including the need for expanded databases and the need to establish multidisciplinary teams. The authors believe that these obstacles can be overcome and that neural networks have a very important role in future medical decision support and the patient management systems employed in routine medical practice.

Decision Theory↗
PubMed2006

Asian Tobacco Education and Cancer Awareness Research Special Population Network. A model for reducing Asian American cancer health disparities.

Asian Americans are the fastest growing and the second largest foreign-born ethnic group in the United States. Cancer is a leading cause of death among Asian Americans. The Asian Tobacco Education and Cancer Awareness Research (ATECAR) Special Population Network, Center for Asian Health, aimed to reduce or eliminate cancer health disparities in these diverse, underserved populations in Pennsylvania, New Jersey, Delaware, and New York. The ATECAR logic model was adapted from a variety of conceptual frameworks to develop and implement the network's multifaceted cancer health disparities research, training, awareness, and outreach programs. The model was the basis for the developmental phases of the network that included (1) needs assessment, infrastructure, and partnership building; (2) intervention research, training, and mentorship; and (3) evaluation, dissemination, and diffusion. Community involvement occurred at every operational level to ensure program and network sustainability. Between 2000 and 2005, the ATECAR network consisted of 88 partners, representing a cross-section of Asian communities, academia, cancer centers, and health service agencies, ensuring a viable infrastructure for the network's multidimensional cancer health disparities programs. ATECAR's research covered tobacco control, cancer prevention and intervention, and clinical trials. More than 22 research projects were conducted and their results disseminated in peer-reviewed journals. ATECAR also trained 76 junior researchers and special population investigators and 1014 community professionals in disparity issues. ATECAR's multimedia cancer awareness education program reached over 116,000 Asians. The ATECAR network's achievements have had a profound impact on Asian Americans and established a trend toward reducing cancer health disparities, especially among underserved Asian Americans. Cancer 2006. (c) 2006 American Cancer Society.

Asian↗
PubMed2004

Direct neural network application for automated cell recognition.

BACKGROUND: Automated cell recognition from histologic images is a very complex task. Traditionally, the image is segmented by some methods chosen to suit the image type, the objects are measured, and then a classifier is used to determine cell type from the object's measurements. Different classifiers have been used with reasonable success, including neural networks working with data from morphometric analysis. METHODS: Image data of cells were input directly into neural networks to determine the feasibility of direct classification by using pixel intensity information. Several types of neural network and their ability to work with cells in a complex patterned background were assessed for a variety of images and cell types and for the accuracy of classification. RESULTS: Inflammatory cells from animal biomaterial implants in rabbit paravertebral muscle were imaged in histologic sections. Simple, three-layer, fully connected, back-propagation neural networks and four-layer networks with two layers of a shared-weights neural network were most successful at classifying the cells from the images, with 97% and 98% correct recognition rates, respectively. CONCLUSIONS: The high accuracy recognition rate shows the potential for direct classification of visual image pixel data by neural networks.

Animals↗
PubMed1997

Neural network modeling of the level of observation decision in an acute psychiatric ward.

Patients in an acute psychiatric ward need to be observed with varying levels of closeness. We report a series of experiments in which neural networks were trained to model this "level of observation" decision. One hundred eighty-seven such clinical decisions were used to train and test the networks which were evaluated by a multitrial v-fold cross-validation procedure. One neural network modeling approach was to break down the decision process into four subproblems, each of which was solved by a perceptron unit. This resulted in a hierarchical perceptron network having a structure that was equivalent to a sparsely connected two-layer perceptron. Neural network approaches were compared with nearest neighbor, linear regression, and naive Bayes classifiers. The hierarchical and sparse neural networks were the most accurate classifiers. This shows that the decision process is nonlinear, that neural nets can be more accurate than other statistical approaches, and that hierarchical decomposition is a useful methodology for neural network design.

Acute Disease↗
PubMed1997

Ventilation mode recognition using artificial neural networks.

This study investigated the capabilities of artificial neural networks to identify spontaneous and pressure support ventilation modes from gas flow and airway pressure signals. After receiving written informed consent, flow and pressure waveforms were recorded from 13 patients undergoing general anesthesia. During analysis, the inspiratory phase of each breath was extracted and normalized in amplitude and wavelength. Neural networks were configured to input flow, pressure, or both waveforms and to output the ventilatory mode. Neural network training was accomplished with data from 500 breaths obtained from 7 patients. Neural network performance was tested with 433 breaths from the remaining 6 patients. Networks using flow, pressure, and both waveforms recognized correctly 78% (337), 97% (423), and 100% (433) of the test waveforms, respectively. Results indicate that neural networks can be used effectively for breathing pattern recognition and encourage the application of neural networks in other types of respiratory pattern recognition problems.

Aged↗
PubMed1999

Classification of asthmatic breath sounds: preliminary results of the classifying capacity of human examiners versus artificial neural networks.

For continuous monitoring of the respiratory condition of patients, e.g., at the intensive care unit, computer assistance is required. Existing mechanical devices, such as the peak expiratory flow meter, provide only with incidental measurements. Moreover, such methods require cooperation of the patient, which at, e.g., the ICU is usually not possible. The evaluation of complicated phenomena such as asthmatic respiratory sounds may be accomplished by use of artificial neural networks. To investigate the merit of artificial neural networks, the capacities of neural networks and human examiners to classify breath sounds were compared in this study. Breath sounds were in vivo recorded from 50 school-age children with asthma and from 10 controls. Sound intervals with a duration of 20 seconds were randomly sampled from asthmatics during exacerbation, asthmatics in remission, and controls. The samples were digitized and related to peak expiratory flow. From each interval, two full breath cycles were selected. Of each selected breath cycle, a Fourier power spectrum was calculated. The so-obtained set of spectral vectors was classified by means of artificial neural networks. Humans evaluated graphic displays of the spectra. Human examiners could not clearly discriminate between the three groups by inspecting the spectrograms. Classification by self-classifying neural networks confirmed the existence of at least three classes; however, discrimination of 11 classes seemed more appropriate. Good results were obtained with supervised networks: as much as 95% of the training vectors could be classified correctly, and 43% of the test vectors. The three patient groups, as discriminated in advance, do not correspond with three sharply separated sets of spectrograms. More than three classes seem to be present. Humans cannot take up the spectral complexity and showed negative classification results. Artificial neural networks, however, are able to handle classification tasks and show positive results.

Acoustics↗
PubMed2000

Artificial neural networks compared to factor analysis for low-dimensional classification of high-dimensional body fat topography data of healthy and diabetic subjects.

Subcutaneous adipose tissue thickness was measured in 590 healthy subjects at 15 specific body sites by means of the new optical device, lipometer, providing a high-dimensional and partly highly intercorrelated set of data, which had been analyzed by factor analysis previously. N-2-N back-propagation neural networks are able to perform low-dimensional display of high-dimensional data as a special application. We report about the performance of such a 15-2-15 network and compare its results with the output of factor analysis. As test data for verification, measurement values on women with proven diabetes mellitus type II (NIDDM) are used. Surprisingly our 15-2-15 neural network is able to reproduce the classification pattern resulting from factor analysis very precisely. After extracting the network weights the classification of new subjects is even more simple with the neural network as compared with factor analysis. In addition, the network weights are able to cluster highly correlated body sites nicely to different groups, corresponding to different regions of the human body. Thus, the analysis of these weights provides additional information about the structure of the data. Therefore, N-2-N networks seem to be a good alternative method for analyzing high-dimensional data with strong intercorrelation.

Adipose Tissue↗
PubMed1996

Modeling Hermissenda: II. Effects of variations in type-B cell excitability, synaptic strength, and network architecture.

Because the Hermissenda eye is relatively simple and its cells well characterized, it provides an attractive preparation for detailed computational analysis. To examine the neural mechanisms of learning in this system, we developed multicompartmental models of the type-A and type-B photoreceptors, simulated the eye, and asked three questions: First, how do conductance changes affect cells in a network as compared with those in isolation; second, what are the relative contributions of increases in B-cell excitability and synaptic strength to network output; and third, how do these contributions vary as a function of network architecture? We found that reductions in the type-B cells of two K+ currents, IA and IC, differentially affected the type-B cells themselves, with IC reductions increasing firing rate (excitability) in response to light, and IA reductions increasing quantal output (synaptic strength) onto postsynaptic targets. Increases in either type-B cel excitability or synaptic strength, induced directly or indirectly, each suppressed A-cell photoresponses, and the combined effect of both changes occurring together was greater than either alone. To examine the effects of network architecture, we compared the full network with a simple feedforward B-A pair and intermediate configurations. Compared with a feedforward pair, the complete network exhibited greater A-cell sensitivity to B-cell changes. This was due to many factors, including an increased number of B-cells (which increased B-cell impact on A-cells), A-B feedback inhibition (which slowed both cell types and altered spike timing relationships), and B-B lateral inhibition (which reduced B-cell sensitivity to intrinsic biophysical modifications). These results suggest that an emergent property of the network is an increase both in the rate of information acquisition ("learning") and in the amount of information that can be stored ("memory").

Animals↗
PubMed1993

The dynamics of sparse random networks.

Recurrent neural networks with full symmetric connectivity have been extensively studied as associative memories and pattern recognition devices. However, there is considerable evidence that sparse, asymmetrically connected, mainly excitatory networks with broadly directed inhibition are more consistent with biological reality. In this paper, we use the technique of return maps to study the dynamics of random networks with sparse, asymmetric connectivity and nonspecific inhibition. These networks show three qualitatively different kinds of behavior: fixed points, cycles of low period, and extremely long cycles verging on aperiodicity. Using statistical arguments, we relate these behaviors to network parameters and present empirical evidence for the accuracy of this statistical model. The model, in turn, leads to methods for controlling the level of activity in networks. Studying random, untrained networks provides an understanding of the intrinsic dynamics of these systems. Such dynamics could provide a substrate for the much more complex behavior shown when synaptic modification is allowed.

Animals↗
PubMed1992

Personal computer system for ECG ST-segment recognition based on neural networks.

A personal computer system for electrocardiogram (ECG) ST-segment recognition is developed based on neural networks. The system consists of a preprocessor, neural networks and a recogniser. The adaptive resonance theory (ART) is employed to implement the neural networks in the system, which self-organise in response to the input ECG. Competitive and co-operative interaction among neurons in the neural networks makes the system robust to noise. The preprocessor detects the R points and divides the ECG into cardiac cycles. Each cardiac cycle is fed into the neural networks. The neural networks then address the approximate locations of the J point and the onset of the T-wave (T(on)). The recogniser determines the respective ranges in which the J and T(on) points lie, based on the locations addressed. Within those ranges, the recogniser finds the exact locations of the J and T(on) points either by a change in the sign of the slope of the ECG, a zero slope or a significant change in the slope. The ST-segment is thus recognised as the portion of the ECG between the J and T(on) points. Finally, the appropriateness of the length of the ST-segment is evaluated by an evaluation rule. As the process goes on, the neural networks self-organise and learn the characteristics of the ECG patterns which vary with each patient.(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Systems↗
PubMed1999

Gait control system for functional electrical stimulation using neural networks.

In functional electrical stimulation (FES) systems for restoring walking in spinal cord injured (SCI) individuals, hand switches are the preferred method for controlling stimulation timing. Through practice the user becomes an 'expert' in determining when stimulation should be applied. Neural networks have been used to 'clone' this expertise but these applications have used small numbers of sensors, and their structure has used a binary output, giving rise to possible controller oscillations. It was proposed that a three-layer structure neural network with continuous function, using a larger number of sensors, including 'virtual' sensors, can be used to 'clone' this expertise to produce good controllers. Using a sensor set of ten force sensors and another of 13 'virtual' kinematic sensors, a good FES control system was constructed using a three-layer neural network with five hidden nodes. The sensor set comprising three sensors showed the best performance. The accuracy of the optimum three-sensor set for the force sensors and the virtual kinematic sensors was 90% and 93%, respectively, compared with 81% and 77% for a heel switch. With 32 synchronised sensors, binary neural networks and continuous neural networks were constructed and compared. The networks using continuous function had significantly fewer oscillations. Continuous neural networks offer the ability to generate good FES controllers.

Electric Stimulation↗
PubMed1996

Comparison of shoe insole materials by neural network analysis.

The effects of two insole materials within the shoe are compared using neural network analysis. Seven male subjects without locomotor disorders walk on a treadmill at a controlled speed and cadence wearing a common shoe and no socks, under three conditions; these are two types of insole of the same thickness, and a no insole condition. Pressure-related data from under the foot, within the shoe, are obtained by the MICRO-EMED system during walking. A back-propagation neural network is trained to associate sets of pressure-related data with the insole conditions. Subsequently neural network analysis is performed to reveal the abstract rules that govern the decision-making processes within the neural network, based on the synergistic interactions between the measured variables. Data are also analysed using ANOVA. The neural network analysis finds trends in the way in which the trained neural network responds. The interpretation of those trends gives a delicate description of the dynamic behaviour of the insoles despite the fact that no significant differences are found using ANOVA. It is concluded that neural network analysis can distinguish between insole behaviour during use, even though these differences are not significantly different based on statistical tests.

Adult↗
PubMed1995

Detection of lung nodules in digital chest radiographs using artificial neural networks: a pilot study.

Radiologists can fail to detect up to 30% of pulmonary nodules in chest radiographs. A back-propagation neural network was used to detect lung nodules in digital chest radiographs to assist radiologists in the diagnosis of lung cancer. Regions of interest (ROIs) that contained nodules and normal tissues in the lung were selected from digitized chest radiographs by a previously developed computer-aided diagnosis (CAD) scheme. Different preprocessing techniques were used to produce input data to the neural network. The performance of the neural network was evaluated by receiver operating characteristic (ROC) analysis. We found that subsampling of original 64- x 64-pixel ROIs to smaller 8- x 8-pixel ROIs provides the optimal preprocessing for the neural network to distinguish ROIs containing nodules from false-positive ROIs containing normal regions. The neural network was able to detect obvious nodules very well with an Az value (area under ROC curve) of 0.93, but was unable to detect subtle nodules. However, with a training method that uses different orientations of the original ROIs, we were able to improve the performance of the neural network to detect subtle nodules. Artificial neural networks have the potential to serve as a useful classifier to help to eliminate the false-positive detections of the CAD scheme.

False Positive Reactions↗
PubMed1990

A cost analysis of film image management and four PACS based on different network protocols.

Picture Archive and Communication Systems (PACS), which allow the electronic acquisition, storage, transportation, and viewing of medical images, hold the eventual promise of reduced costs, improved image-management logistics, and ultimately, improved patient care. But at what point in the future will PACS really cost less than film-based image management for a given hospital size; and how are these costs affected by the choice of the digital communication network? To address these questions, a static differential cost model has been constructed. PAC systems based on two high-speed networks (less than 150 megabytes per second Mbps) and two low-speed networks, as well as film, were considered for five different sized hospitals (ranging from 15,000 to 125,000 procedures per year) and two time periods (1995 and 2000). PACS equipment was assumed to have a payoff of five years. The model considered all capital and supply costs and personnel costs for the PACS and for film storage and retrieval. It did not consider any possible cost savings from logistics improvement likely to result from the adoption of a PACS. Based on the assumptions outlined, high-speed-network PACS are less costly than those based on low-speed networks for all scenarios considered. Further, even though all possible PACS cost savings were not considered, high-speed network PACS appear to be less costly than film for hospitals larger than 60,000 procedures in 1995 and larger than 15,000 in 2000, while low-speed-network PACS should cost less than film for 60,000 and 30,000 procedure hospitals in 1995 and 2000 respectively.

Computer Communication Networks↗
PubMed1991

Wide area networks for teleradiology.

Teleradiology networks transmit digital radiographic images from one location to another. These networks are wide area networks. Teleradiology networks are used for diagnostic purposes and preview tasks. Wide area networks for teleradiology use public service switching. The use of fiber optics networks provide reduced costs and increased flexibility. An example is presented that compares the cost of teleradiology networks.

Computer Communication Networks↗