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At least 1,315 records · Page 73Linked to original sources
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↗
PubMed1999

A taxonomy of health networks and systems: bringing order out of chaos.

OBJECTIVE: To use existing theory and data for empirical development of a taxonomy that identifies clusters of organizations sharing common strategic/structural features. DATA SOURCES: Data from the 1994 and 1995 American Hospital Association Annual Surveys, which provide extensive data on hospital involvement in hospital-led health networks and systems. STUDY DESIGN: Theories of organization behavior and industrial organization economics were used to identify three strategic/structural dimensions: differentiation, which refers to the number of different products/services along a healthcare continuum; integration, which refers to mechanisms used to achieve unity of effort across organizational components; and centralization, which relates to the extent to which activities take place at centralized versus dispersed locations. These dimensions were applied to three components of the health service/product continuum: hospital services, physician arrangements, and provider-based insurance activities. DATA EXTRACTION METHODS: We identified 295 health systems and 274 health networks across the United States in 1994, and 297 health systems and 306 health networks in 1995 using AHA data. Empirical measures aggregated individual hospital data to the health network and system level. PRINCIPAL FINDINGS: We identified a reliable, internally valid, and stable four-cluster solution for health networks and a five-cluster solution for health systems. We found that differentiation and centralization were particularly important in distinguishing unique clusters of organizations. High differentiation typically occurred with low centralization, which suggests that a broader scope of activity is more difficult to centrally coordinate. Integration was also important, but we found that health networks and systems typically engaged in both ownership-based and contractual-based integration or they were not integrated at all. CONCLUSIONS: Overall, we were able to classify approximately 70 percent of hospital-led health networks and 90 percent of hospital-led health systems into well-defined organizational clusters. Given the widespread perception that organizational change in healthcare has been chaotic, our research suggests that important and meaningful similarities exist across many evolving organizations. The resulting taxonomy provides a new lexicon for researchers, policymakers, and healthcare executives for characterizing key strategic and structural features of evolving organizations. The taxonomy also provides a framework for future inquiry about the relationships between organizational strategy, structure, and performance, and for assessing policy issues, such as Medicare Provider Sponsored Organizations, antitrust, and insurance regulation.

American Hospital Association↗
PubMed1997

Transeuropean network for the provision of added-VAlue Services in Telemedicine--(VAST-Net).

Populations most likely to use telemedicine are usually located far away from ISDN networks in contrast to providers of telemedical services who normally dwell in urban centers. VAST-Net is a consortium that aspires to reach such populations by introducing added-value ISDN-based telemedical services across Europe, through networking a small number of selected, strategically distributed, points-of-care and by focusing on a series of well defined health care provision scenaria. This will be achieved by relying on: (1) the Euro-ISDN infrastructure, (2) satellite based ISDN medical networks (e.g. the INMARSAT/VSAT based MERMAID network), (3) private ISDN networks. The VAST-Net telemedical network will be initially operated on a small but transnational scale in order to assess: (1) the acceptance of telemedical services by end users, (2) the economic viability of the telemedical sector, especially in an international context (3) the legal implications of telemedical practice (4) the regulatory framework within which telemedicine will be developed in the E.U. Based on this assessment the VAST-Net Consortium will proceed to develop appropriate business plans that will allow PTTs and other Telecom operators, medical groups, insurance companies and public health authorities to offer transnationally ISDN-based telemedical services. Potential forms of cooperation may include: (1) "Framework agreements" between PTTs, telemedical service providers (e.g. doctors, hospitals etc.) and "bulk users" (insurance companies, public health authorities), (2) PTT provision of "telecommunications backbones" over which competing telemedical networks can be organised, (3) organisation of a European Economic Interest Group (EEIG), modeled upon international companies such as INMARSAT, that will undertake the operation of VAST-Net.

Computer Communication Networks↗
PubMed1998

University medical information network--past, present, and future.

The University Medical Information Network (UMIN), established in 1989, is a network service organization for national university hospitals in Japan. It has provided various medical information services to medical professionals, including database, electronic mail, and news services. Although its initial network was constructed as a closed network using N1 protocol, it now adopts TCP/IP protocol and is open to other medical professionals via the Internet. The next UMIN network system is planned to be constructed as a secure virtual closed network on the Internet, using cipher technology, and to provide secure information services to national university hospitals via the closed network, and to other medical professional via the Internet. User friendly interface and flexible system development were made possible by adopting TCP/IP, and the number of users dramatically increased accordingly. However, the database, software design, and human organizations developed in the N1 era have now proven to be of great value, and contribute to todayís flourishing state of the UMIN.

Computer Communication Networks↗
PubMed1999

[The cooperation system for drug analysis by computer network].

Generally, the drugs are analyzed by the forensic medicine department in the most of autopsy cases. The forensic medicine department cannot always perform toxicological analysis because a shortage of a verified personnel, appropriate instruments, and financial resources. In order to overcome these difficulties and lack of resources, we developed a computer network called ml-poison. This network consists of the staff of forensic medicine, hospitals, governmental agencies and research facilities of police departments, etc. This network offers immediate and valuable information and expertise to these inexperienced in dealing with cases of poisoning. In cases in which the toxicological analysis cannot be performed in a facility, the network will recommend a facility where the analysis can be performed. Up this point, the network order system has assisted in many cases of poisoning. Although the effectiveness of the network for toxicological analysis has been proven, we still must deal with severed difficult problems: 1. The number of facilities assisting network orders is limited. 2. Who will pay the expenses involved in the analysis. 3. How to maintain security of the system. 4. What agency will assume responsibility for the management of the system.

Computer Communication Networks↗
PubMedDate not supplied

The use of available social support networks by older blacks.

Research has indicated that strong social networks positively influence the health of older Black adults. However, scales used in studies to measure social support do not reveal whether these networks are actually activated. The purpose of this descriptive study was to describe the reasons older Blacks with chronic illnesses did not utilize available support networks in their health care management. Following a community-based survey of 383 older Blacks, where each participant had a social support score completed from the Lubben Social Network Scale, a smaller group of 116 Blacks was selected to participate in a qualitative interview. It was found that 59 out of the 116 older Blacks had high social support scores on the Lubben Scale, but only 40 of these individuals reported the use of support networks for the maintenance of their health regimen. These findings illustrated that some older Blacks with strong support networks chose to function independently of their social support networks in the maintenance of their health.

Black or African American↗
PubMed2000

Controlling activity fluctuations in large, sparsely connected random networks.

Controlling activity in recurrent neural network models of brain regions is essential both to enable effective learning and to reproduce the low activities that exist in some cortical regions such as hippocampal region CA3. Previous studies of sparse, random, recurrent networks constructed with McCulloch-Pitts neurons used probabilistic arguments to set the parameters that control activity. Here, we extend this work by adding an additional, biologically appropriate, parameter to control the magnitude and stability of activity oscillations. The new constant can be considered to be the rest conductance in a shunting model or the threshold when subtractive inhibition is used. This new parameter is critical for large networks run at low activity levels. Importantly, extreme activity fluctuations that act to turn large networks totally on or totally off can now be avoided. We also show how the size of external input activity interacts with this parameter to affect network activity. Then the model based on fixed weights is extended to estimate activities in networks with distributed weights. Because the theory provides accurate control of activity fluctuations, the approach can be used to design a predictable amount of pseudorandomness into deterministic networks. Such nonminimal fluctuations improve learning in simulations trained on the transitive inference problem.

Computer Simulation↗
PubMed2001

The use of neural networks and logistic regression analysis for predicting pathological stage in men undergoing radical prostatectomy: a population based study.

PURPOSE: Clinical under staging occurs in 40% to 60% of patients who undergo radical prostatectomy for prostate cancer. To decrease under staging several methods of predicting pathological stage preoperatively have been developed based on statistical logistic regression analysis and neural networks. To our knowledge none has been validated in our homogeneous regional patient population to date. We created logistic regression and neural network models, and implemented and adapted them into our practice. We also compared the 2 methods to determine their value and practicality in daily clinical practice. We present the results of our novel approach for predicting pathological staging of prostate adenocarcinoma. MATERIALS AND METHODS: Between 1986 and 1999, 600 white men from the Aragon region of Spain underwent surgery for prostate cancer; of whom 468 were selected for study. Predictive study variables included patient age, clinical stage, biopsy Gleason score and preoperative prostate specific antigen (PSA). The predicted result included in analysis was organ confined or nonorgan confined disease. Data were analyzed by multivariate logistic regression and a supervised neural network (multilayer perceptron and radial basis function). Results were compared by comparing the areas under the receiver operating characteristics curves. RESULTS: We generated 5 logistic regression models. The model created with clinical staging, Gleason biopsy score and PSA distributed in 5 categories (p <0.001) with an area under the receiver operating characteristics curve of 0.840 proved to be most predictive of pathological stage. Similarly of the 6 neural network models evaluated the radial basis function model, which included age, clinical stage, Gleason biopsy score and preoperative PSA distributed in 5 categories with an area under the curve of 0.882, proved the most predictive but not superior to the logistic regression model. The difference in the area under the curves in the 2 chosen models was 0.042 (p = 0.1). CONCLUSIONS: It is possible to generate useful predictive models of organ confined disease using logistic regression or neural networks with high indexes of clinical and statistical validity. However, using these variables neural networks did not prove to be better than logistic regression analysis. Therefore, better predictive variables must be identified, preferably nonlinear characteristics with respect to the probability of organ confined tumor, to generate better predictive models using neural networks.

Aged↗
PubMed2003

[Impact of new information and communication technologies (NTIC) on hospital administration and patient management. Care Network for Diagnosing and Treating Neurologic Emergencies].

The combination of specialised in-patient management, the development of diagnostic and therapeutic tools, the standardisation of procedures and organisation of the dies will allow for improving the prognosis of neurological patients seen in emergency situations. In order to achieve this objective, the Networks for Diagnosing and Treating Neurological Emergencies (RAIDS-UN) in Franche-Comté (FC) aim to better the quality of in-patient management for emergency on-set neurological pathologies such as traumatic brain injuries and strokes, the preparation and structuring of the dies for diagnosis and treatment, and assistance in neurology decision making. Based upon the needs of networks which approach the problem from two different directions--the RAIDS-UN/FC network (for neurological emergencies) and the RAIDS-UN/AVC network (for prevention and treatment of strokes)--they should both be able to benefit from new information and communication technologies (ICTs) in order to promote and support innovations in practice and contribute to the improvement in quality and health promotion effectiveness. In light of these developments, one may better understand the significance of the School of Medicine's role in training experts, advisors and supervisors as well as in providing a high level of quality and effective continuing education. Since 2001, the expansion of the RAIDS-UN networks has emphasised the need for the development of new professional specialties arising on the border between technology and medicine. The operation of these networks requires a strong partnership with health professionals and strategically relies upon a regional dynamic interaction which includes the hospitals, the city, the institutions and the university. It is in this manner that the RAIDS-UN networks will support other initiatives such as the Towards Unity for Health project.

Community Networks↗
PubMed2003

HIV lipodystrophy case definition using artificial neural network modelling.

OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under the ROC curve. Their average sensitivity and specificity were 72.4 and 71.2%, as compared with 73.0 and 62.9% for logistic regression, respectively (area under the ROC curve, 0.784 vs 0.748). The discriminating performance of the neural networks was largely unaffected when built excluding 13 parameters that patients may not have readily available. The average sensitivity and specificity of the neural networks remained the same when metabolic variables were also considered (total 60 items) without a clear advantage against logistic regression (overall accuracy 71.8%). The performance of networks considering also body composition variables was similar to that of logistic regression (overall accuracy 78.5% for both). CONCLUSIONS: Neural networks may offer a means to improve the discriminating performance for HIV lipodystrophy, when only clinical data are available and a rapid approximate diagnostic decision is needed. In this context, information on metabolic parameters is apparently not helpful in improving the diagnosis of HIV lipodystrophy, unless imaging and body composition studies are also obtained.

Absorptiometry, Photon↗
PubMed1992

High-performance computing, high-speed networks, and configurable computing environments: progress toward fully distributed computing.

The next several years will see the maturing of a collection of technologies that will enable fully and transparently distributed computing environments. Networks will be used to configure independent computing, storage, and I/O elements into "virtual systems" that are optimal for solving a particular problem. This environment will make the most powerful computing systems those that are logically assembled from network-based components and will also make those systems available to a widespread audience. Anticipating that the necessary technology and communications infrastructure will be available in the next 3 to 5 years, we are developing and demonstrating prototype applications that test and exercise the currently available elements of this configurable environment. The Lawrence Berkeley Laboratory (LBL) Information and Computing Sciences and Research Medicine Divisions have collaborated with the Pittsburgh Supercomputer Center to demonstrate one distributed application that illuminates the issues and potential of using networks to configure virtual systems. This application allows the interactive visualization of large three-dimensional (3D) scalar fields (voxel data sets) by using a network-based configuration of heterogeneous supercomputers and workstations. The specific test case is visualization of 3D magnetic resonance imaging (MRI) data. The virtual system architecture consists of a Connection Machine-2 (CM-2) that performs surface reconstruction from the voxel data, a Cray Y-MP that renders the resulting geometric data into an image, and a workstation that provides the display of the image and the user interface for specifying the parameters for the geometry generation and 3D viewing. These three elements are configured into a virtual system by using several different network technologies. This paper reviews the current status of the software, hardware, and communications technologies that are needed to enable this configurable environment. These interdependent technologies include: (1) user interface and application program construction methodologies, (2) the interprocess communication (IPC) mechanisms used to connect the software modules of the application, (3) the network protocols and interface hardware used by the IPC for communicating between modules running on separate and independent computing system elements, (4) the telecommunications infrastructure that provides the low-level data transfer functions for the networks that connect the geographically distributed elements used by the application, and (5) the nature of the functional elements that will be connected to form virtual systems.

Computer Communication Networks↗
PubMed2004

[Study on the application of artificial neural network in analysing the risk factors of diabetes mellitus].

OBJECTIVE: To study the use of neural network in determining the risk factors of diseases. METHODS: With back-propagation neural network (BP network) as fitting model based upon data gathered from an epidemiological survey on diabetes mellitus and under the network structure of 22-6-1, the mean impact value (MIV) for each input variables and sequencing the factors according to their absolute MIVs were calculated. The results from BP network with multiple logistic regression analysis and log-linear model for united actions between factors were compared with optimizing Levenberg-Marquardt algorithm. RESULTS: By BP network analysis, the sequence of importance for the risk factors of diabetes mellitus became: faster pulse, diabetes mellitus family history, living longer in the investigated area, with medical record of nephropathy, having higher ratio for waist-to-hip, being male, with medical records of diseases as hyperlipoproteinmia, coronary heart disease, hypertension, high diastolic pressure, higher income, do no drink alcohol, age, higher systolic pressure, less educated, body mass index, with medical records of other diseases, physical exercise related to jobs smoking, occupation, with medical record for cerebrovascular disease, with medical record for liver disease etc. However, only 7 factors were statistically significant in multiple logistic regression analysis. The sequence of their importance appeared as: pulse, diabetes mellitus family history, the medical record of nephropathy, waist-to-hip ratio, the medical record of hypertension, work-place related exercise and age. The sequences of importance were almost the same between the two while the difference could partly be explained by the interaction among risk factors through log-linear model. CONCLUSION: Neural network could be used to analyze the risk factors of diseases and could assimilate more complicated relationships (main effects and interactions) between inputs and outputs, better than using the traditional methods.

Adult↗
PubMed2004

Inferring dynamic properties of biochemical reaction networks from structural knowledge.

Functional properties of biochemical networks depend on both the network structure and the kinetic parameters. Extensive data on metabolic network topologies have been collected in databases, but much less information is available about the kinetic constants or metabolite concentrations. Depending on the values of these parameters, metabolic fluxes and control coefficients may vary within a wide range. Nevertheless, some of the parameters may have little influence on the observables of interest. We address the question whether, despite uncertainty about kinetic parameters, probabilistic statements can be made about dynamic network features. To this end, we perform a variability analysis of the parameters: assuming that the parameters follow statistical distributions, we compute the resulting distributions of the network properties like metabolic fluxes, concentrations, or control coefficients by Monte Carlo simulation. In this manner, we study systematically the possible distributions arising from typical topologies of biochemical networks such as linear chains, branched networks, and signaling and gene expression cascades. This analysis reveals how much information about dynamic behavior can be drawn from structural knowledge.

Biochemistry↗
PubMed2005

Selection of predictor variables for pneumonia using neural networks and genetic algorithms.

BACKGROUND: Artificial neural networks (ANN) can be used to select sets of predictor variable that incorporate nonlinear interactions between variables. We used a genetic algorithm, with selection based on maximizing network accuracy and minimizing network input-layer cardinality, to evolve parsimonious sets of variables for predicting community-acquired pneumonia among patients with respiratory complaints. METHODS: ANN were trained on data from 1044 patients in a training cohort, and were applied to 116 patients in a testing cohort. Chromosomes with binary genes representing input-layer variables were operated on by crossover recombination, mutation, and probabilistic selection based on a fitness function incorporating both network accuracy and input-layer cardinality. RESULTS: The genetic algorithm evolved best 10-variable sets that discriminated pneumonia in the training cohort (ROC areas, 0.838 for selection based on average cross entropy (ENT); 0.954 for selection based on ROC area (ROC)), and in the testing cohort (ROC areas, 0.847 for ENT selection; 0.963 for ROC selection), with no significant differences between cohorts. Best variable sets based on the genetic algorithm using ROC selection discriminated pneumonia more accurately than variable sets based on stepwise neural networks (ROC areas, 0.954 versus 0.879, p = 0.030), or stepwise logistic regression (ROC areas, 0.954 versus 0.830, p = 0.000). Variable sets of lower cardinalities were also evolved, which also accurately discriminated pneumonia. CONCLUSION: Variable sets derived using a genetic algorithm for neural networks accurately discriminated pneumonia from other respiratory conditions, and did so with greater accuracy than variables derived using stepwise neural networks or logistic regression in some cases.

Algorithms↗
PubMed2005

[A primary study on simulation of fracture strength based on BP neural networks].

OBJECTIVE: To model the relationship between stimulating stress and fracture strength using BP neural networks, and to provide a theoretical basis for accurate prediction of the rate of fracture healing. METHODS: The bilateral tibiae in New Zealand rabbits were osteotomized and fixed by stress-relaxation plate(SRP) and rigid plate(RP), respectively. The stress shielding rate and bending strength of the healing fractures were measured at 2 to 48 weeks postoperatively. A BP neural network was constructed and trained using the experimental data of the stress-relaxation group. Then the trained network was used for simulation to predict fracture strength of the two groups from the stress at the fracture site. RESULTS: With the input of the data that has been used to train the network, fracture strength similar to those measured in experiment was calculated from the BP neural network. However, poor results were obtained with the input of new data. CONCLUSION: BP neural network can be used to investigate the influence of various factors on fracture healing quantitatively, and to predict the rate of healing. However, the model still needs to be perfected. More experimental or clinical data are needed to train the network

Animals↗
PubMed1992

Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects.

The value of PET as an objective diagnostic tool for dementia may depend on the degree to which abnormal metabolic patterns can be detected by quantitative classification methods. In these studies, a neural-network classifier based on coarse region of interest analyses was used to classify normal and abnormal FDG-PET scans. The performance of neural networks and of an expert reader were evaluated by cross-validation testing. When the "abnormal" class was represented by subjects with clinical diagnoses of "Probable Alzheimer's," the areas under the relative-operating-characteristic (ROC) curves were 0.85 and 0.89 for the neural network and the expert reader, respectively. When testing with abnormal subjects represented by "Possible AD" cases, ROC areas for both the network and the expert were 0.81. The neural network out-performed discriminant analysis. It is concluded that PET has potential for the detection of abnormal brain function in dementing diseases, and that the combination of neural networks and PET is a useful diagnostic tool. Despite the low-resolution "view" afforded the neural network, its performance was nearly equivalent to that of an expert reader.

Adult↗
PubMed2005

Applications of Artificial Neural Networks in integrated water management: fiction or future?

An Artificial Neural Network (ANN) is nowadays recognized as a very promising tool for relating input data to output data. It is said that the possibilities of artificial neural networks are unlimited. Here we focus on the potential role of neural networks in integrated water management. An Artificial Neural Network (ANN) is a mathematical methodology which describes relations between cause (input data) and effects (output data) irrespective of the process laying behind and without the need for making assumptions considering the nature of the relations. The applications are widespread and vary from optimization of measuring networks, operational water management, prediction of drinking water consumption, on-line steering of wastewater treatment plants and sewage systems, up to more specific applications such as establishing a relationship between the observed erosion of groyne field sediments and the characteristics of passing vessels on the river Rhine. Especially where processes are complex, neural networks can open new possibilities for understanding and modelling these kinds of complex processes. Besides explaining the method of ANN this paper shows different applications. Three examples have been worked out in more detail. An intelligent monitoring system is shown for the on-line prediction of water consumption, ANN are successfully used for sludge cost monitoring and optimizing wastewater treatment and the usage of ANN is shown in optimizing and monitoring water quality measuring networks. An ANN appears to be a multiuse and powerful tool for modelling complex processes.

Conservation of Natural Resources↗
PubMed2006

Contemporary, emerging, and ratified wireless security standards: an update for the networked dental office.

Wireless networking is not new to contemporary dental offices around the country. Wireless routers and network cards have made access to patient records within the office handy and, thereby, saving valuable chair side time and increasing productivity. As is the case with any rapidly developing technology, wireless technology also changes with the same rate. Unless, the users of the wireless networking understand the implications of these changes and keep themselves updated periodically, the office network will become obsolete very quickly. This update of the emerging security protocols and pertaining to ratified wireless 802.11 standards will be timely for the contemporary dentist whose office is wirelessly networked. This article brings the practicing dentist up-to-date on the newer versions and standards in wireless networking that are changing at a fast pace. The introduction of newer 802.11 standards like super G, Super AG, Multiple Input Multiple Output (MIMO), and pre-n are changing the pace of adaptation of this technology. Like any other rapidly transforming technology, information pertaining to wireless networking should be a priority for the contemporary dentist, an eventual end-user in order to be a well-informed and techno-savvy consumer.

Computer Security↗