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At least 145 records · Page 8Linked to original sources
PubMed2007

Network analysis in public health: history, methods, and applications.

Network analysis is an approach to research that is uniquely suited to describing, exploring, and understanding structural and relational aspects of health. It is both a methodological tool and a theoretical paradigm that allows us to pose and answer important ecological questions in public health. In this review we trace the history of network analysis, provide a methodological overview of network techniques, and discuss where and how network analysis has been used in public health. We show how network analysis has its roots in mathematics, statistics, sociology, anthropology, psychology, biology, physics, and computer science. In public health, network analysis has been used to study primarily disease transmission, especially for HIV/AIDS and other sexually transmitted diseases; information transmission, particularly for diffusion of innovations; the role of social support and social capital; the influence of personal and social networks on health behavior; and the interorganizational structure of health systems. We conclude with future directions for network analysis in public health.

Behavioral Research↗
PubMed2000

Neural network modeling of risk assessment in child protective services.

The advantages of using neural network methodology for the modeling of complex social science data are demonstrated, and neural network analysis is applied to Washington State Child Protective Services risk assessment data. Neural network modeling of the association between social worker overall assessment of risk and the 37 separate risk factors from the State of Washington Risk Assessment Matrix is shown to provide case classification results superior to linear or logistic multiple regression. The improvement in case prediction and classification accuracy is attributed to the superiority of neural networks for modeling nonlinear relationships between interacting variables; in this respect the mathematical framework of neural networks is a better approximation to the actual process of human decision making than linear, main effects regression. The implications of this modeling advantage for evaluating social science data within the framework of ecological theories are discussed.

Child↗
PubMed1997

Distributed neural networks for biomedical research.

To facilitate the application of neural networks in bio-medical sciences, we have developed a new client-server neural network concept that enhances user operation and reduces processing time. The client component allows users to enter all information required to create and control a complete neural network project. The server component provides the actual tools for neural network development. The server is able to distribute the burden of neural network processing among remote machines, if available. To date, the system has been applied successfully to problems in the of chromosome recognition and horse gait analysis domains.

Computer Communication Networks↗
PubMed2005

[Latin American science museums and equity].

Latin America and the Caribbean form a region of great variations yet marked by cultural resemblances. The origins of the region's countries, of their wealth, and also of their problems are quite similar. Indeed, there are clear points of contact in the most basic aspects, such as each society's worldview. Throughout the region there is a very strong democratizing trend that seeks more just and more educated societies. Science plays a key role in the development as a powerful weapon for tolerance and equity and therefore should be disseminated among the greatest possible number of Latin Americans. Red POP the network for the Popularization of Science and Technology in Latin America and the Caribbean - was created to help reach this goal. Under the auspices of ORCYT-UNESCO, Red POP is an interactive network of centers and programs that work to bring science and technology to the public at large. It fosters exchange, skill-acquisition, and resource use among its members. Based on the network's experience, we explore to what extent science museums favor equity in their home societies.

Education↗
PubMed1995

Medical image processing utilizing neural networks trained on a massively parallel computer.

While finding many applications in science, engineering, and medicine, artificial neural networks (ANNs) have typically been limited to small architectures. In this paper, we demonstrate how very large architecture neural networks can be trained for medical image processing utilizing a massively parallel, single-instruction multiple data (SIMD) computer. The two- to three-orders of magnitude improvement in processing time attainable using a parallel computer makes it practical to train very large architecture ANNs. As an example we have trained several ANNs to demonstrate the tomographic reconstruction of 64 x 64 single photon emission computed tomography (SPECT) images from 64 planar views of the images. The potential for these large architecture ANNs lies in the fact that once the neural network is properly trained on the parallel computer the corresponding interconnection weight file can be loaded on a serial computer. Subsequently, relatively fast processing of all novel images can be performed on a PC or workstation.

Computer Systems↗
PubMed2001

Exploring complex networks.

The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.

Models, Theoretical↗
PubMed1998

Embedded neural networks: exploiting constraints.

Using concepts and tools of embodied cognitive science, we investigate the implications of embedding neural networks in a physical structure, the body of a robot. Embedding a neural network in a body provides constraints that can be exploited for learning. We show that the constraints are given by the environment and object properties, the agent's morphology, the agent's motor system and specific ways of interacting with the objects. We argue that designing embedded neural networks implies (a) understanding these constraints, and (b) exploiting them, i.e., designing neural networks such that they-one way or other-incorporate the constraints. This in turn results in cheap and simple networks that are suited for the task environment, and have real-time responses. Moreover, this constraint-based approach provides new perspectives on two fundamental problems of cognitive science: focus-of-attention and object constancy. The main arguments are illustrated with a series of case studies with simulated and physical mobile robots that are controlled by hand-designed as well as evolved neural networks.

Journal Article↗
PubMed2004

Application of formal methods to biological regulatory networks: extending Thomas' asynchronous logical approach with temporal logic.

Based on the discrete definition of biological regulatory networks developed by René Thomas, we provide a computer science formal approach to treat temporal properties of biological regulatory networks, expressed in computational tree logic. It is then possible to build all the models satisfying a set of given temporal properties. Our approach is illustrated with the mucus production in Pseudomonas aeruginosa. This application of formal methods from computer science to biological regulatory networks should open the way to many other fruitful applications.

Computational Biology↗
PubMed1993

Creating the integrated information infrastructure for the 21st century at the University of Washington Warren G. Magnuson Health Sciences Center.

Successful integrated information systems implementation requires an effective marriage of technology and information resources in response to critical institutional needs. The University of Washington technical infrastructure, developed over the past five years, includes ubiquitous, high-speed network access throughout the Health Sciences Center and hospitals, agreement on network standards and protocols, uniform interface to common databases (character-based and GUI) and network availability of a variety of databases and information resources at no charge to the individual. As a result of this heavy institutional investment in technical infrastructure, our implementation process will focus on expanding the number of available resources as well as developing and refining tools and services to enhance the utility of electronic information resources. Above all we will study and develop strategies for dealing with the myriad of information policy issues which confront and confound us all today.

Academic Medical Centers↗
PubMed2003

The need for scientists and judges to work together: regarding a new European network.

Is it always true to say that science is, by definition, universal whilst laws and the courts which apply them are a classic state and national expression? Yes and no. In recent years a new scenario has opened all over the world. Courts intervene more and more in disputes on matters related to scientific procedures in the biological field. In doing so the courts' decisions are affected by scientific issues and ways of reasoning and, on the other hand, affect the scientific field and its way of reasoning. While the old matter of bioethics was still alive and while judges were improving their skill in dealing with hard matters, like refusal of medical treatments, abortion, euthanasia et cetera, a new challenge appeared on the horizon, the challenge of biological sciences, and especially of the most troubled field of human genetics. A completely new awareness is developing among judges that they belong to an international judiciary community, as informal as it is real. Such a community is, even at an embryonic stage, sufficiently universal to be able to come together with the international scientific community. The authors maintain we are in urgent need for new interaction between judges and scientists and of new international means in the light of such cooperation. Judges and jurists need to become better acquainted with scientific questions and learn to exchange ideas with scientists. They also need to set themselves against the latters' conceptual systems and be willing to put their own up for discussion. A European Network for Life Sciences, Health and the Courts is taking its first steps, and judges and scientists are working side by side to tackle the new challenges. The provisional headquarters are located at the University of Pavia (I), Laboratorio di Biologia dello Sviluppo and Collegio Ghislieri (e-mail: enlsc@unipv.it). ENLSC activity is inspired by the following idea: to be against science is as much antiscientific as to be acritically pro-science.

Bioethical Issues↗
PubMed2006

Assessing the effects of human mixing patterns on human immunodeficiency virus-1 interhost phylogenetics through social network simulation.

Geneticists seeking to understand HIV-1 evolution among human hosts generally assume that hosts represent a panmictic population. Social science research demonstrates that the network patterns over which HIV-1 spreads are highly nonrandom, but the effect of these patterns on the genetic diversity of HIV-1 and other sexually transmitted pathogens has yet to be thoroughly examined. In addition, interhost phylogenetic models rarely account explicitly for genetic diversity arising from intrahost dynamics. This study outlines a graph-theoretic framework (exponential random graph modeling, ERGM) for the estimation, inference, and simulation of dynamic partnership networks. This approach is used to simulate HIV-1 transmission and evolution under eight mixing patterns resembling those observed in empirical human populations, while simultaneously incorporating intrahost viral diversity. Models of parametric growth fit panmictic populations well, yielding estimates of total viral effective population on the order of the product of infected host size and intrahost effective viral population size. Populations exhibiting patterns of nonrandom mixing differ more widely in estimates of effective population size they yield, however, and reconstructions of population dynamics can exhibit severe errors if panmixis is assumed. I discuss implications for HIV-1 phylogenetics and the potential for ERGM to provide a general framework for addressing these issues.

Evolution, Molecular↗
PubMed1993

[Social medicine within medical education--experience abroad].

The trend to classify ("teaching" of social medicine more within the framework of development of primary or community-based health care is described. The apparent inefficiency of theoretical (lecture-based) presentation of social medicine asks for more weight to combine such a course with practical experience in the field of community-based health care. The growing importance of associations like the NETWORK (Community Oriented Educational Institutions for the Health Sciences) and of the International Clinical Epidemiology Network (INCLEN) is discussed.

Community Medicine↗
PubMed2002

Highly clustered scale-free networks.

We propose a model for growing networks based on a finite memory of the nodes. The model shows stylized features of real-world networks: power-law distribution of degree, linear preferential attachment of new links, and a negative correlation between the age of a node and its link attachment rate. Notably, the degree distribution is conserved even though only the most recently grown part of the network is considered. As the network grows, the clustering reaches an asymptotic value larger than that for regular lattices of the same average connectivity and similar to the one observed in the networks of movie actors, coauthorship in science, and word synonyms. These highly clustered scale-free networks indicate that memory effects are crucial for a correct description of the dynamics of growing networks.

Journal Article↗
PubMedDate not supplied

The use of artificial neural networks in biomedical technologies: an introduction.

Artificial neural networks (NN) are systems than can learn. In the most common situation, an operator trains the system on a set of input and output data belonging to a particular category. If new data of the same category, but not in the training set, are presented to the system, the NN can use the learned data to predict outcomes without any specific programming relating to the category of events involved. The fields of application of NN have increased dramatically in the past few years. Originally, the NN technique was mainly in the hands of computer programming specialists and the applications concentrated on tasks such as decision systems and signal processing. However, this picture has changed due to the emergence of user-friendly NN software for personal computers. A large variety of possible NN applications now exist for non-computer specialists. Thus, with only a very modest knowledge of the theory behind neural networks, it is possible to attack complicated problems in a researcher's own area of specialty with the NN technique. This is especially true in the field of medical technology, the topic of this review. The review is divided into three sections: 1) an elementary introduction to useful NN methods; 2) a review of the most important applications of the NN technique to this point in time; 3) a summary of available computer details that would be needed for a beginner in this field.

Humans↗