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PubMed2001

Categorization of fetal heart rate patterns using neural networks.

Digitized data from CTG (cardiotocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 min duration, have been used in the categorization process. In the first part of the study, selected CTG data corresponding to 10 typical cases were used for purely auto associative unsupervised training of a Self-Organizing Map Neural Network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring.

Cardiotocography↗

PubMed2006

Biological network design strategies: discovery through dynamic optimization.

An important challenge in systems biology is the inherent complexity of biological network models, which complicates the task of relating network structure to function and of understanding the conceptual design principles by which a given network operates. Here we investigate an approach to analyze the relationship between a network structure and its function using the framework of optimization. A common feature found in a variety of biochemical networks involves the opposition of a pair of enzymatic chemical modification reactions such as phosphorylation-dephosphorylation or methylation-demethylation. The modification pair frequently adjusts biochemical properties of its target, such as activating and deactivating function. We applied optimization methodology to study a reversible modification network unit commonly found in signal transduction systems, and we explored the use of this methodology to discover design principles. The results demonstrate that different sets of rate constants used to parameterize the same network topology represent different compromises made in the resulting network operating characteristics. Moreover, the same topology can be used to encode different strategies for achieving performance goals. The ability to adopt multiple strategies may lead to significantly improved performance across a range of conditions through rate modulation or evolutionary processes. The optimization framework explored here is a practical approach to support the discovery of design principles in biological networks.

Computer Simulation↗

PubMed2005

Spontaneous evolution of modularity and network motifs.

Biological networks have an inherent simplicity: they are modular with a design that can be separated into units that perform almost independently. Furthermore, they show reuse of recurring patterns termed network motifs. Little is known about the evolutionary origin of these properties. Current models of biological evolution typically produce networks that are highly nonmodular and lack understandable motifs. Here, we suggest a possible explanation for the origin of modularity and network motifs in biology. We use standard evolutionary algorithms to evolve networks. A key feature in this study is evolution under an environment (evolutionary goal) that changes in a modular fashion. That is, we repeatedly switch between several goals, each made of a different combination of subgoals. We find that such "modularly varying goals" lead to the spontaneous evolution of modular network structure and network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Such switching between related goals may represent biological evolution in a changing environment that requires different combinations of a set of basic biological functions. The present study may shed light on the evolutionary forces that promote structural simplicity in biological networks and offers ways to improve the evolutionary design of engineered systems.

Algorithms↗

PubMed2004

Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source.

Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.

Air Pollutants↗

PubMed2003

Duplication models for biological networks.

Are biological networks different from other large complex networks? Both large biological and nonbiological networks exhibit power-law graphs (number of nodes with degree k, N(k) approximately k(-beta)), yet the exponents, beta, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein-protein interaction networks) and is fundamentally different from the mechanisms thought to dominate the growth of most nonbiological networks (such as the Internet). The preferential choice models used for nonbiological networks like web graphs can only produce power-law graphs with exponents greater than 2. We use combinatorial probabilistic methods to examine the evolution of graphs by node duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.

Internet↗

PubMed2001

Sexual networks and sexually transmitted infections: a tale of two cities.

Research on risk behaviors for sexually transmitted infections (STIs) has revealed that they seldom correspond with actual risk of infection. Core groups of people with high-risk behavior who form networks of people linked by sexual contact are essential for STI transmission, but have been overlooked in epidemiological studies. Social network analysis, a subdiscipline of sociology, provides both the methods and analytical techniques to describe and illustrate the effects of sexual networks on STI transmission. Sexual networks of people from Colorado Springs, Colorado, and from Winnipeg, Manitoba, Canada, infected with chlamydia during a 6-month period were compared. In Winnipeg, 442 networks were identified, comprising 571 cases and 663 contacts, ranging in size from 2 to 20 individuals; Colorado Springs data yielded 401 networks, comprising 468 cases and 700 contacts, ranging in size from 2 to 12 individuals. Taking differing partner notification methods and the slightly smaller population size in Colorado Springs into account, the networks from both places were similar in both size and structure. These smaller, sparsely linked networks, peripheral to the core, may form the mechanism by which chlamydia can remain endemic, in contrast with larger, more densely connected networks, closer to the core, which are associated with steep rises in incidence.

Adolescent↗

PubMed2001

Interorganizational relationships among HIV/AIDS service organizations in Baltimore: a network analysis.

A wide variety of organizations has become involved in providing medical and social services to people living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Although there is much interest among policymakers, service providers, and clients in coordination among HIV/AIDS service organizations, few studies have used network analytic tools to examine existing systems of HIV-related care. In an effort to fill this gap, this study used network analysis methods to describe several aspects of the interorganizational relationships among 30 HIV/AIDS service agencies in Baltimore, Maryland. Client referrals to other organizations, client referrals from other organizations, exchange of information about shared clients, formal written linkage agreements for client referrals, and joint programs were each examined as a distinct type of network tie, with each the basis of a separate network among these 30 organizations. All of the networks except the one based on joint programs were relatively well connected, with most organizations either directly or indirectly linked. Most of the interorganizational collaboration occurred on a rather ad hoc basis for the purposes of meeting the more immediate needs presented by clients. Highly structured coordination involving substantial investment of resources and ongoing interagency activities appeared to be less common. The findings from this study also suggest that the providers in Baltimore tend to work directly with others as client needs arise rather than negotiating through "clearinghouse" types of organizations. Of the 30 HIV/AIDS service organizations, 5 were highly central in at least four of the five different types of networks. These five organizations--each having a critical role in the continuum of care--may be considered the most central core of the HIV/AIDS service delivery network in Baltimore. These organizations tend to be those that have been created specifically to provide HIV-related services or that specialize in HIV/AIDS care. This research can help policymakers understand how an HIV-related service delivery network may function and delineate key features of a network. In all communities, this type of assessment is critical to designing interventions to promote collaboration that are feasible within the context of existing interorganizational relationships. This type of data also has implications for informing activities to build the capacity of HIV/AIDS service organizations.

Acquired Immunodeficiency Syndrome↗

PubMed2001

Neural network model of gene expression.

Many natural processes consist of networks of interacting elements that, over time, affect each other's state. Their dynamics depend on the pattern of connections and the updating rules for each element. Genomic regulatory networks are networks of this sort. In this paper we use artificial neural networks as a model of the dynamics of gene expression. The significance of the regulatory effect of one gene product on the expression of other genes of the system is defined by a weight matrix. The model considers multigenic regulation including positive and/or negative feedback. The process of gene expression is described by a single network and by two linked networks where transcription and translation are modeled independently. Each of these processes is described by different network controlled by different weight matrices. Methods for computing the parameters of the model from experimental data are discussed. Results computed by means of the model are compared with experimental observations. Generalization to a 'black box' concept, where the molecular processes occurring in the cell are considered as signal processing units forming a global regulatory network, is discussed.-Vohradský, J. Neural network model of gene expression.

Gene Expression Regulation↗

PubMed2003

Estimation of pulmonary artery occlusion pressure by an artificial neural network.

OBJECTIVE: We hypothesized that an artificial neural network, interconnected computer elements capable of adaptation and learning, could accurately estimate pulmonary artery occlusion pressure from the pulsatile pulmonary artery waveform. SETTING: University medical center. SUBJECTS: Nineteen closed-chest dogs. INTERVENTIONS: Pulmonary artery waveforms were digitally sampled before conventional measurements of pulmonary artery occlusion pressure under control conditions, during infusions of serotonin or histamine, or during volume loading. Individual beats were parsed or separated out. Pulmonary artery pressure, its first time derivative, and the beat duration were used as neural inputs. The neural network was trained by using 80% of all samples and tested on the remaining 20%. For comparison, the regression between pulmonary artery diastolic pressure and pulmonary artery occlusion pressure was developed and tested using the same data sets. As a final test of generalizability, the neural network was trained on data obtained from 18 dogs and tested on data from the remaining dog in a round-robin fashion. MEASUREMENTS AND MAIN RESULTS: The correlation coefficient between the pulmonary artery diastolic pressure estimate of pulmonary artery occlusion pressure and measured pulmonary artery occlusion pressure was.75, whereas that for the neural network estimate of pulmonary artery occlusion pressure was.97 (p <.01 for difference between pulmonary artery diastolic pressure and pulmonary artery occlusion pressure estimates). The pulmonary artery diastolic pressure estimate of pulmonary artery occlusion pressure showed a bias of 0.097 mm Hg (limits of agreement -7.57 to 7.767 mm Hg), whereas the neural network estimate of pulmonary artery occlusion pressure showed a bias of -0.002 mm Hg (-2.592 to 2.588 mm Hg). There was no significant change in the bias of the neural network estimate over the range of values tested. In contrast, the bias for the pulmonary artery diastolic pressure estimate significantly increased with the increasing magnitude of the pulmonary artery occlusion pressure. During round-robin testing, the neural network estimate of pulmonary artery occlusion pressure showed suboptimal performance (correlation coefficient between estimated and measured pulmonary artery occlusion pressure.59). CONCLUSIONS: A neural network can accurately estimate pulmonary artery occlusion pressure over a wide range of pulmonary artery occlusion pressure under conditions that alter pulmonary hemodynamics. We speculate that artificial neural networks could provide accurate, real-time estimates of pulmonary artery occlusion pressure in critically ill patients.

Animals↗

PubMed2004

An artificial neural network ensemble to predict disposition and length of stay in children presenting with bronchiolitis.

BACKGROUND: Artificial neural networks apply complex non-linear functions to pattern recognition problems. An ensemble is a 'committee' of neural networks that usually outperforms single neural networks. Bronchiolitis is a common manifestation of viral lower respiratory tract infection in infants and toddlers. OBJECTIVE: To train artificial neural network ensembles to predict the disposition and length of stay in children presenting to the Emergency Department with bronchiolitis. METHODS: A specifically constructed database of 119 episodes of bronchiolitis was used to train, validate, and test a neural network ensemble. We used EasyNN 7.0 on a 200 Mhz pentium PC with a maths co-processor. The ensemble of neural networks constructed was subjected to fivefold validation. Comparison with actual and predicted dispositions was measured using the kappa statistic for disposition and the Kaplan-Meier estimations and log rank test for predictions of length of stay. RESULTS: The neural network ensembles correctly predicted disposition in 81% (range 75-90%) of test cases. When compared with actual disposition the neural network performed similarly to a logistic regression model and significantly better than various 'dumb machine' strategies with which we compared it. The prediction of length of stay was poorer, 65% (range 60-80%), but the difference between observed and predicted lengths of stay were not significantly different. CONCLUSION: Artificial neural network ensembles can predict disposition for infants and toddlers with bronchiolitis; however, the prediction of length of hospital stay is not as good.

Bronchiolitis↗

PubMed2003

Whom do they serve? Community responsiveness among hospitals affiliated with health systems and networks.

BACKGROUND: As the US hospital sector becomes more consolidated, concerns have been raised about whether participation in health systems and health networks may reduce community hospitals' response to community health needs. OBJECTIVES: The following were examined: (1) whether freestanding hospitals and system- and network-affiliated hospitals differed in their level of community responsiveness; and (2) how systems and networks affect the level of community responsiveness in community hospitals. METHODS: A cross-sectional design was used. The dependent variables included community orientation, provision of community health services, and Medicaid inpatient load. Independent variables were system/network membership and policy and organizational attributes of the health system/network. RESULTS: With few exceptions, a significantly greater involvement of system and network hospitals was found in providing community health services and inpatient services to Medicaid patients, relative to freestanding hospitals. Community health mission of the system/network and the involvement of the system/network in community partnerships or coalitions were positively related to community orientation in member hospitals. Hospitals affiliated with health systems and hospitals affiliated with more diversified systems or networks tended to provide more community health services. Community health mission of the health system or network was related to greater Medicaid inpatient load in member hospitals. CONCLUSIONS: In general, affiliation with health systems and health networks appears to be positively related to community responsiveness in community hospitals. Research future can examine whether such greater community responsiveness is because of the development and improvement of communication channels among elements of health systems and health networks and the ability of health systems and health networks to build a platform of general, administrative services to link various constituencies.

Community Health Services↗

PubMed2000

Mean field theory for asymmetric neural networks.

The computation of mean firing rates and correlations is intractable for large neural networks. For symmetric networks one can derive mean field approximations using the Taylor series expansion of the free energy as proposed by Plefka. In asymmetric networks, the concept of free energy is absent. Therefore, it is not immediately obvious how to extend this method to asymmetric networks. In this paper we extend Plefka's approach to asymmetric networks and in fact to arbitrary probability distributions. The method is based on an information geometric argument. The method is illustrated for asymmetric neural networks with sequential dynamics. We compare our approximate analytical results with Monte Carlo simulations for a network of 100 neurons. It is shown that the quality of the approximation for asymmetric networks is as good as for symmetric networks.

Models, Neurological↗

PubMed2001

Topological properties of citation and metabolic networks.

Topological properties of "scale-free" networks are investigated by determining their spectral dimensions d(S), which reflect a diffusion process in the corresponding graphs. Data bases for citation networks and metabolic networks together with simulation results from the growing network model [A.-L. Barabasi and R. Albert, Science 286, 509 (1999)] are probed. For completeness and comparisons lattice, random and small-world models are also investigated. We find that d(S) is around 3 for citation and metabolic networks, which is significantly different from the growing network model, for which d(S) is approximately 7.5. This signals a substantial difference in network topology despite the observed similarities in vertex-order distributions. In addition, the diffusion analysis indicates that the citation networks are treelike in structure, whereas the metabolic networks contain many loops.

Animals↗

PubMed2005

Coarse-graining and self-dissimilarity of complex networks.

Can complex engineered and biological networks be coarse-grained into smaller and more understandable versions in which each node represents an entire pattern in the original network? To address this, we define coarse-graining units as connectivity patterns which can serve as the nodes of a coarse-grained network and present algorithms to detect them. We use this approach to systematically reverse-engineer electronic circuits, forming understandable high-level maps from incomprehensible transistor wiring: first, a coarse-grained version in which each node is a gate made of several transistors is established. Then the coarse-grained network is itself coarse-grained, resulting in a high-level blueprint in which each node is a circuit module made of many gates. We apply our approach also to a mammalian protein signal-transduction network, to find a simplified coarse-grained network with three main signaling channels that resemble multi-layered perceptrons made of cross-interacting MAP-kinase cascades. We find that both biological and electronic networks are "self-dissimilar," with different network motifs at each level. The present approach may be used to simplify a variety of directed and nondirected, natural and designed networks.

Algorithms↗

PubMed2003

Prediction of cyclosporine dosage in patients after kidney transplantation using neural networks.

This paper proposes the use of neural networks for individualizing the dosage of cyclosporine A (CyA) in patients who have undergone kidney transplantation. Since the dosing of CyA usually requires intensive therapeutic drug monitoring, the accurate prediction of CyA blood concentrations would decrease the monitoring frequency and, thus, improve clinical outcomes. Thirty-two patients and different factors were studied to obtain the models. Three kinds of networks (multilayer perceptron, finite impulse response (FIR) network, and Elman recurrent network) and the formation of neural-network ensembles are used in a scheme of two chained models where the blood concentration predicted by the first model constitutes an input to the dosage prediction model. This approach is designed to aid in the process of clinical decision making. The FIR network, yielding root-mean-square errors (RMSEs) of 52.80 ng/mL and mean errors (MEs) of 0.18 ng/mL in validation (10 patients) showed the best blood concentration predictions and a committee of trained networks improved the results (RMSE = 46.97 ng/mL, ME = 0.091 ng/mL). The Elman network was the selected model for dosage prediction (RMSE = 0.27 mg/Kg/d, ME = 0.07 mg/Kg/d). However, in both cases, no statistical differences on the accuracy of neural methods were found. The models' robustness is also analyzed by evaluating their performance when noise is introduced at input nodes, and it results in a helpful test for models' selection. We conclude that neural networks can be used to predict both dose and blood concentrations of cyclosporine in steady-state. This novel approach has produced accurate and validated models to be used as decision-aid tools.

Administration, Oral↗

PubMed2006

Universal approximation using incremental constructive feedforward networks with random hidden nodes.

According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient, and it may be difficult to train networks with nondifferential activation functions such as threshold networks. Unlike conventional neural network theories, this paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions g : R --> R and the activation functions for RBF nodes can be any integrable piecewise continuous functions g : R --> R and integral of R g(x)dx not equal to 0. The proposed incremental method is efficient not only for SFLNs with continuous (including nondifferentiable) activation functions but also for SLFNs with piecewise continuous (such as threshold) activation functions. Compared to other popular methods such a new network is fully automatic and users need not intervene the learning process by manually tuning control parameters.

Algorithms↗

PubMed2003

Connecting our resources: Louisiana's approach to community health network development.

Louisiana's rural community health systems are in crisis because of pressures fueled by the rising costs of health care, sustained poor health status, state budget shortfalls and changes in priorities, and a sliding rural economy. The development of community health networks is providing new infrastructure and capacity for communities to reprioritize, formulate innovative partnerships, and leverage new resources. Successful elements of Louisiana's network development experience include community commitment to engage in study and action; the availability of capable and motivated technical assistance; an approach that involves open-engagement, community-driven decision-making; and data-driven problem definition, prioritization, and solutions. Louisiana's experiences illustrate the benefits of developing networks along with, or as a result of, a community health plan. When a community owns its health improvement plan, it is more likely to support the new network as a structure for implementation. Broad-scale participation is also a principle of success. When social service agencies are included along with health agencies, more comprehensive strategies result, and they bring additional resources, resulting in more holistic solutions. The cases of 2 networks are presented as illustrations. One involves the facilitation of a community planning process for an existing network. The plan helped to expand the network's community connections and support and provided the content for a successful application for a Health Resources and Services Administration Community Access Program grant. In the second case, a new network was developed, and it leveraged federal funds from the federal Office of Rural Health Policy's Network Development Grant Program.

Community Health Planning↗

PubMed2003

User requirements and understanding of public health networks in England.

BACKGROUND: The movement of public health professionals from health authorities to primary care trusts has increased their isolation and dependence on public health networks for communication. METHODS: A cross sectional survey of 60 public health professionals working in England was performed to determine their understanding of the term "public health network" and to explore the functions that they would like these networks to perform. It also assessed their attitudes towards a national network and towards individual, local, and national web sites to support these networks. RESULTS: The most popular functions were the support of CPD/education, the identification of expertise and maximisation of scarce resources, information sharing, and efficient information/knowledge management. The local and national networks and their web sites should provide information on current projects of the network and searches to identify people, expertise, and reports. CONCLUSION: Public health professionals have a similar but broader understanding of the term "public health network" than that of the government with greater emphasis on sharing of information. The network is more likely to be successful if its priorities are maximising scarce resources, identification of expertise, CPD/education, and knowledge management.

Adolescent↗