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PubMed1991

An ultrafast network for communication of radiologic images.

The three most difficult problems in making picture archiving and communication systems (PACS) a clinical reality in radiology are image archiving, very high-resolution display stations, and high-speed networking. This article considers high-speed image transmission through a high-capacity network. Our laboratory has tested several commercially available high-speed networks over the past year. Only one of these networks (UltraNet) has adequate throughput and capacity potential necessary for our PACS. The focus of this experiment is to determine the throughput and capacity characteristics of this star topology networking scheme as they relate to the operation of a PACS in the clinical environment. A large-scale test was done to gauge network performance for three networking configurations modeling those in a PACS: duplex, parallel, and relay. Ten computers used in our PACS (Sun 3 and 4 computers) were connected with UltraNet. For point-to-point throughput (half-duplex model), the network delivers up to 3.1 megabytes/sec for Sun 3 computers and 6.8 megabytes/sec for the Sun Sparcserver 490. As regards capacity considerations (parallel model), five parallel image transfer processes generated a maximum of 13.9 megabytes/sec through the network. Only a slight degradation in individual process throughput was observed (1.4%). With regard to shared access to high-contention resources on the PACS network (e.g., archive servers), this network demonstrated equal sharing of server networking capacity between the various client computers. With the encouraging results of this experiment, we believe that the UltraNet network will be sufficient for the image communication requirements of our PACS. We are proceeding with the implementation of UltraNet as the high-speed backbone of our extended PACS network.

Computers↗
PubMed1994

Simulation studies of a wide area health care network.

There is an increasing number of efforts to install wide area health care networks. Some of these networks are being built to support several applications over a wide user base consisting primarily of medical practices, hospitals, pharmacies, medical laboratories, payors, and suppliers. Although on-line, multi-media telecommunication is desirable for some purposes such as cardiac monitoring, store-and-forward messaging is adequate for many common, high-volume applications. Laboratory test results and payment claims, for example, can be distributed using electronic messaging networks. Several network prototypes have been constructed to determine the technical problems and to assess the effectiveness of electronic messaging in wide area health care networks. Our project, Health Link, developed prototype software that was able to use the public switched telephone network to exchange messages automatically, reliably and securely. The network could be configured to accommodate the many different traffic patterns and cost constraints of its users. Discrete event simulations were performed on several network models. Canonical star and mesh networks, that were composed of nodes operating at steady state under equal loads, were modeled. Both topologies were found to support the throughput of a generic wide area health care network. The mean message delivery time of the mesh network was found to be less than that of the star network. Further simulations were conducted for a realistic large-scale health care network consisting of 1,553 doctors, 26 hospitals, four medical labs, one provincial lab and one insurer. Two network topologies were investigated: one using predominantly peer-to-peer communication, the other using client-server communication.(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Communication Networks↗
PubMed1997

Current keratoconus detection methods compared with a neural network approach.

PURPOSE: Four videokeratographic methods for keratoconus detection were compared with a neural network approach. METHODS: A classification neural network for keratoconus screening was designed to detect the presence of keratoconus (KC) or keratoconus suspects (KCS); a separate cone severity network graded the severity of conelike topography patterns consistent with KC or KCS. Three hundred TMS-1 examinations (Tomey) were randomly divided into training and test sets. Ten topographic indexes were network inputs. Nine categories were used: normal, astigmatism, KC, KCS, contact lens-induced warpage, pellucid marginal degeneration, photorefractive keratectomy, radial keratotomy, and penetrating keratoplasty. KC was subdivided into KC1 (mild), KC2 (moderate), and KC3 (advanced). There were three outputs for the classification network (KC, KCS, and OTHER); target output values of 0 = OTHER, 0.25 = KCS, 0.5 = KC1, 0.75 = KC2, and 1.0 = KC3 were used for the severity network. RESULTS: The best-trained classification network had 100% accuracy, specificity, and sensitivity for the test set. The severity network had mean outputs (+/-standard deviation) of OTHER = 0.02+/-0.02, KCS = 0.21+/-0.05, KC1 = 0.52+/-0.17, KC2 = 0.74+/-0.12, and KC3 = 0.91+/-0.15. The severity network output for all categories was well correlated to the keratoconus prediction index (R = 0.892, P < 0.0001). The classification network had an overall accuracy and specificity significantly better (P < or = 0.005) than the Klyce/Maeda keratoconus index (KCI) test, the Rabinowitz test (K & I-S), and simulated keratometry (average Sim K). However, there were no significant differences in keratoconus sensitivity between the classification network, KCI, and K & I-S. The sensitivity and specificity of average Sim K were significantly worse than those of the other tests. The classification network had significantly better sensitivity (P < 0.001) and specificity (P = 0.025) for KCS detection than the K & I-S. CONCLUSIONS: The neural networks completely distinguished KC from KCS and from topographies that resembled KC. The network approach equaled the sensitivity of currently used tests for keratoconus detection and outperformed them in terms of accuracy and specificity.

Cornea↗
PubMed2003

The relationship between social networks and occupational and self-care functioning in people with psychosis.

BACKGROUND: Relatively few studies have examined relationships between the social networks of people with psychotic disorder and other aspects of their functioning. The aim of this paper is to describe the social networks of people with psychosis and to investigate relationships between social networks and personal and occupational functioning, taking account of illness course. METHODS: A two-phase epidemiological survey of persons with psychosis was conducted in four predominantly urban areas of Australia. A census and screen for psychosis was followed by a semi-structured interview of a stratified random sample of participants to assess their functioning. Data relating to functioning and social networks from 908 individuals (most with a diagnosis of schizophrenia) were analysed using structural equation modelling (SEM). RESULTS: The majority of people with psychosis (67 %) had a network comprising of family and friends, 15 % were defined as having a family-dominated network, 11 % a friends-dominated network and 7 % of participants were defined as socially isolated (no family or friends). Participants who had friends and family in their network (12 %) or who had a family-dominated network (7 %) were more likely to be in full-time employment compared with those with a friends-dominated network (4 %) or those who were socially isolated (5 %). Dysfunction in self-care was more frequently reported among socially isolated people (50 %) and those with family-dominated networks (47 %) than among those with friends-dominated networks (35 %) and those who had friends and family in their social network (23 %). SEM revealed a strong association between social integration and functioning (r = 0.71), even after controlling for illness course. Social integration was defined as having contact with family and/or friends and functioning was defined as having employment and no difficulties in self-care. Male gender was associated with poorer self-care, and female gender was slightly, but significantly, associated with a greater likelihood of having friends. CONCLUSION: There is a strong relationship between social networks and functioning after taking account of course of illness. That is, the presence of family and friends is generally associated with better self-care and employment. Interventions that are targeted at improving social relationships are likely to have a positive impact on self-care and occupational functioning (and vice versa).

Employment↗
PubMed2006

Characterisation of UV-cured acrylate networks by means of hydrolysis followed by aqueous size-exclusion combined with reversed-phase chromatography.

UV-cured networks prepared from mixtures of di-functional (polyethylene-glycol di-acrylate) and mono-functional (2-ethylhexyl acrylate) acrylates were analysed after hydrolysis, by aqueous size-exclusion chromatography coupled to on-line reversed-phase liquid-chromatography. The mean network density and the fraction of dangling chain ends of these networks were varied by changing the concentration of mono-functional acrylate. The amount and the molar-mass distribution of the polyethylene-glycol chains between cross-links (M(XL)) and polyacrylic acid (PAA) backbone chains (the so-called kinetic chain length (kcl)) in the different acrylate networks were determined quantitatively. The molar-mass distribution of kcl revealed an almost linear dependence on the concentration of mono-functional acrylate. Analysis of the starting materials showed a significant concentration of mono-functional polyethylene-glycol acrylate. In combination with the analysis of the extractables of the UV-cured networks (polymers not attached to the network, impurities that originate from the photo-initiator and unreacted monomers), more insight in the total network structure was obtained. It was shown that the UV-cured networks contain only small fractions of residual compounds. With these results, the chemical network structure for the different UV-cured acrylate polymers was expressed in network parameters such as the number of PAA units which are cross-linked, the degree of cross-linking, and the network density, which is the molar concentration of effective network chains between cross-links per volume of the polymers. The mean molar mass of chains between chemical network junctions (M(C)) was calculated and compared with results obtained from solid-state NMR and DMA. The mean molar mass of chains between network junctions as determined by these methods was similar.

Acrylates↗
PubMed1997

The network shifts of elderly immigrants: the case of Soviet Jews in Israel.

This article analyzes the network dynamics of 259 Soviet immigrants, aged 62-92, who arrived in Israel during the recent wave of mass immigration. The study uses a Quick Cluster procedure with structural network characteristics as criterion variables to identify four primary network types among the study population: (1) kin network, (2) family-intensive network, (3) friend-focused network, and (4) diffuse-tie network. The pattern of shifts from pre-immigration network type to post-immigration network type reveals that the primary shifts were from the non-familial based network types to the familial-based types, and within the familial types from a wider kin network to a more restricted family-intensive network. These shifts reflect a move from networks of choice to networks of necessity on the part of many older immigrants.

Journal Article↗
PubMed1998

An empirical assessment of rural community support networks for individuals with severe mental disorders.

The community support network has been well-established as a requirement for community treatment of individuals with severe mental disorders. This network generally consists of a multidisciplinary set of organizations that interrelate in some manner with individuals in the community. The question of coordination within this network has been much discussed; however little published research has empirically examined the types and extent of coordination among network organizations. In particular, little attention has been given to community support networks in rural communities. In each of seven rural counties, information was obtained on inter-actions among organizations in the community support network. These networks were analyzed to yield information on network density and centralization. Using measures of centrality, the most central organizations in each network were identified. Exchanges of information were the most common type of interaction among organizations in each network. Client referrals occurred less frequently, and sharing of resources was an even rarer phenomenon. Network analysis of community support networks provides an objective perspective on the structure of community support networks. An understanding of exchange among organizations within these networks is of value to administrators, clinicians, and planners interested in achieving greater effectiveness, as well as to patients, their families, and advocacy groups concerned with access and quality of care.

Catchment Area, Health↗
PubMed2002

Benign dermoscopic network patterns in dysplastic melanocytic nevi.

BACKGROUND/AIMS: Epiluminescence microscopy (ELM) is a non-invasive clinical technique, which by employing the optical phenomenon of oil immersion makes surface structures of the skin accessible for in vivo examination and provides additional criteria for the diagnosis of pigment skin lesions (PSLs). Many ELM criteria have been described. One of the most important ELM criteria is the pigment network (PN). OBJECTIVE: The aim of this study is to identify benign ELM (dermoscopic) network patterns of dysplastic melanocytic nevi (DMN). METHODS: This study included 907 dysplastic melanocytic nevi in 178 patients. Prior to biopsy, each lesion was photographed with oil immersion, and the images were viewed on a high-resolution compact slide projector. For each PSL, the ELM Network Features and ABCD-score were evaluated. RESULTS AND DISCUSSION: The benign dermoscopic network features in DMN are the presents of a regular PN with delicate lines and margins, which predominantly thins out at the border of the lesion. For DMN, with these features, the mean ABCD score is smaller than ABCD-score for DMNs with irregular, prominent PN and network patches, ending abruptly at the periphery. In DMN with a network predominantly thinning out at the border of the lesion several uniform network patterns were found-diffuse network pattern, patchy network pattern, structureless center pattern, globular center pattern, and pigmented-blotch center pattern. CONCLUSIONS: Benign features of pigment network are regularity, delicacy and thinning out at the border of the lesion. Benign dermoscopic network patterns are diffuse network pattern, patchy network pattern, structureless center pattern, globular center pattern, and pigmented-blotch center pattern. They can be found in DMN with a network predominantly thinning out at the border of the lesion.

Adult↗
PubMed2003

Topology of gene expression networks as revealed by data mining and modeling.

MOTIVATION: Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein-protein interactions, and gene networks that can be derived from gene expression data. In the present paper, we focus on several important issues related to the topology of gene expression networks that have not yet been fully studied. RESULTS: The networks derived from gene expression profiles for both time series experiments in yeast and perturbation experiments in cell lines are studied. We demonstrate that independent from the experimental organism (yeast versus cell lines) and the type of experiment (time courses versus perturbations) the extracted networks have similar topological characteristics suggesting together with the results of other common principles of the structural organization of biological networks. A novel computational model of network growth that reproduces the basic design principles of the observed networks is presented. Advantage of the model is that it provides a general mechanism to generate networks with different types of topology by a variation of a few parameters. We investigate the robustness of the network structure to random damages and to deliberate removal of the most important parts of the system and show a surprising tolerance of gene expression networks to both kinds of disturbance.

Algorithms↗
PubMed2005

A new measure of the robustness of biochemical networks.

MOTIVATION: The robustness of a biochemical network is defined as the tolerance of variations in kinetic parameters with respect to the maintenance of steady state. Robustness also plays an important role in the fail-safe mechanism in the evolutionary process of biochemical networks. The purposes of this paper are to use the synergism and saturation system (S-system) representation to describe a biochemical network and to develop a robustness measure of a biochemical network subject to variations in kinetic parameters. Since most biochemical networks in nature operate close to the steady state, we consider only the robustness measurement of a biochemical network at the steady state. RESULTS: We show that the upper bound of the tolerated parameter variations is related to the system matrix of a biochemical network at the steady state. Using this upper bound, we can calculate the tolerance (robustness) of a biochemical network without testing many parametric perturbations. We find that a biochemical network with a large tolerance can also better attenuate the effects of variations in rate parameters and environments. Compensatory parameter variations and network redundancy are found to be important mechanisms for the robustness of biochemical networks. Finally, four biochemical networks, such as a cascaded biochemical network, the glycolytic-glycogenolytic pathway in a perfused rat liver, the tricarboxylic acid cycle in Dictyostelium discoideum and the cAMP oscillation network in bacterial chemotaxis, are used to illustrate the usefulness of the proposed robustness measure.

Animals↗
PubMed2006

A classification of the fibrin network structures formed from the hereditary dysfibrinogens.

OBJECTIVE: The main objective was to study the relationships of the molecular defects in 38 dysfibrinogens with their fibrin networks. METHODS AND RESULTS: Scanning electron microscopic analyses revealed that all the fibrins formed under the same conditions had networks composed of either normal thickness fibers or thin fibers, accompanied by a variety of alterations in the network structure and characteristics. We classified these fibrin networks into five classes, designated normal, less-ordered, porous A, porous B and lace-like networks. The dysfibrinogens with defects in fibrinopeptide A release or the E:D binding sites formed normal or less-ordered networks, while those with defects in the D:D association formed porous A networks composed of many tapered terminating fibers, despite having fibers of normal width, and containing many pores or spaces. The porous B and lace-like networks were composed of highly branched thin fibers because of defects in the lateral association among protofibrils, and the major difference between them was the porosity of the porous B networks. All the porous B networks were easily damaged by mechanical stress, whereas the lace-like networks retained high resistance to such stress, indicating that the network strength was not dependent on the fiber width, but on the porosity that led to fragility of the network. CONCLUSION: Impairment of the D:D association is the major disturbing factor that leads to the formation of porous fibrin networks. The porosity may be introduced by severe impairment of the D:D association, as well as the lateral association, as has often been observed by extra glycosylation or defects in Ca2+ binding.

Afibrinogenemia↗
PubMed1973

Computer and information networks.

The most basic conclusion coming out of the EDUCOM seminars is that computer networking must be acknowledged as an important new mode for obtaining information and computation (15). It is a real alternative that needs to be given serious attention in current planning and decision-making. Yet the fact is that many institutions are not taking account of networks when they confer on whether or how to replace their main computer. Articulation of the possibilities of computer networks goes back to the early 1960's and before, and working networks have been in evidence for several years now, both commercially and in universities. What is new, however, is the unmistakable recognition-bordering on a sense of the inevitable-that networks are finally practical and here to stay. The visionary and promotional phases of computer networks are over. It is time for hard-nosed comparative analysis (16). Another conclusion of the seminars has to do with the factors that hinder the fuller development of networking. The major problems to be overcome in applying networks to research and education are political, organizational, and economic in nature rather than technological. This is not to say that the hardware and software problems of linking computers and information systems are completely solved, but they are not the big bottlenecks at present. Research and educational institutions must find ways to organize themselves as well as their computers to work together for greater resource sharing. The coming of age of networks takes on special significance as a result of widespread dissatisfactions expressed with the present computing situation. There is a feeling that the current mode of autonomous, self-sufficient operation in the provision of computing and information services is frequently wasteful, deficient, and unresponsive to users' needs because of duplication of effort from one installation to another, incompatibilities, and inadequate documentation, program support, and user assistance. Complaints about the relative lack of uniform standards and the paucity of information on what programs and data are available and how to get and use them are commonplace. The human tendency, when beset by problems such as these, is to seek a savior in the next new technology-networks in this case. But networking does not in and of itself offer a solution to current deficiencies. What it does offer is a promising vehicle with which to bring about important changes in user practices, institutional procedures, and government policy that can lead to effective solutions. Thus more critical than whether networking is developed and applied is how it is developed and applied. For example, networking emphasizes the need for standards and good documentation. Unless effective mechanisms are developed and strong measures taken in networking to ensure that suitable standards and documentation are developed, present inadequacies could get worse, not better.

Computers↗
PubMed2006

Exploration of biological network centralities with CentiBiN.

BACKGROUND: The elucidation of whole-cell regulatory, metabolic, interaction and other biological networks generates the need for a meaningful ranking of network elements. Centrality analysis ranks network elements according to their importance within the network structure and different centrality measures focus on different importance concepts. Central elements of biological networks have been found to be, for example, essential for viability. RESULTS: CentiBiN (Centralities in Biological Networks) is a tool for the computation and exploration of centralities in biological networks such as protein-protein interaction networks. It computes 17 different centralities for directed or undirected networks, ranging from local measures, that is, measures that only consider the direct neighbourhood of a network element, to global measures. CentiBiN supports the exploration of the centrality distribution by visualising central elements within the network and provides several layout mechanisms for the automatic generation of graphical representations of a network. It supports different input formats, especially for biological networks, and the export of the computed centralities to other tools. CONCLUSION: CentiBiN helps systems biology researchers to identify crucial elements of biological networks. CentiBiN including a user guide and example data sets is available free of charge at http://centibin.ipk-gatersleben.de/. CentiBiN is available in two different versions: a Java Web Start application and an installable Windows application.

Computer Graphics↗
PubMed2000

The net effects of the Project NetWork return-to-work case management experiment on participant earnings, benefit receipt, and other outcomes.

The Social Security Administration (SSA) initiated Project NetWork in 1991 to test case management as a means of promoting employment among persons with disabilities. The demonstration, which targeted Social Security Disability Insurance (DI) beneficiaries and Supplemental Security Income (SSI) applicants and recipients, offered intensive outreach, work-incentive waivers, and case management/referral services. Participation in Project NetWork was voluntary. Volunteers were randomly assigned to the "treatment" group or the "control" group. Those assigned to the treatment group met individually with a case or referral manager who arranged for rehabilitation and employment services, helped clients develop an individual employment plan, and provided direct employment counseling services. Volunteers assigned to the control group could not receive services from Project NetWork but remained eligible for any employment assistance already available in their communities. For both treatment and control groups, the demonstration waived specific DI and SSI program rules considered to be work disincentives. The experimental impact study thus measures the incremental effects of case and referral management services. The eight demonstration sites were successful in implementing the experimental design roughly as planned. Project NetWork staff were able to recruit large numbers of participants and to provide rehabilitation and employment services on a substantial scale. Most of the sites easily reached their enrollment targets and were able to attract volunteers with demographic characteristics similar to those of the entire SSI and DI caseload and a broad range of moderate and severe disabilities. However, by many measures, volunteers were generally more "work-ready" than project eligible in the demonstration areas who did not volunteer to receive NetWork services. Project NetWork case management increased average annual earnings by $220 per year over the first 2 years following random assignment. This statistically significant impact, an approximate 11-percent increase in earnings, is based on administrative data on earnings. For about 70 percent of sample members, a third year of followup data was available. For this limited sample, the estimated effect of Project NetWork on annual earnings declined to roughly zero in the third followup year. The findings suggest that the increase in earnings may have been short-lived and may have disappeared by the time Project NetWork services ended. Project NetWork did not reduce reliance on SSI or DI benefits by statistically significant amounts over the 30-42 month followup period. The services provided by Project NetWork thus did not reduce overall SSI and DI caseloads or benefits by substantial amounts, especially given that only about 5 percent of the eligible caseload volunteered to participate in Project NetWork. Project NetWork produced modest net benefits to persons with disabilities and net costs to taxpayers. Persons with disabilities gained mainly because the increases in their earnings easily outweighed the small (if any) reduction in average SSI and DI benefits. For SSA and the federal government as a whole, the costs of Project NetWork were not sufficiently offset by increases in tax receipts resulting from increased earnings or reductions in average SSI and DI benefits. The modest net benefits of Project NetWork to persons with disabilities are encouraging. How such benefits of an experimental intervention should be weighed against costs of taxpayers depends on value judgments of policymakers. Because different case management projects involve different kinds of services, these results cannot be directly generalized to other case management interventions. They are nevertheless instructive for planning new initiatives. Combining case and referral management services with various other interventions, such as longer term financial support for work or altered provider incentives, could produc

Adolescent↗
PubMed2002

Networking between occupational health services, client enterprises and other experts: difficulties, supporting factors and benefits.

This study explores difficulties, supporting factors and benefits of networking to studied enterprises and other network partners (focus on OHS and client enterprises). The study also explores social capital as a resource produced in network relations, and trust as a core dimension of social capital, and trust as a binding element in networking. The study is a mixed methods research (both qualitative and quantitative research materials). The most important supporting factors were: committed and active focus person, teamwork skills, long relationships, familiarity, trust and two-way communication in co-operation, shared goals, norms and values, an equal cost and benefit ratio, and the high quality of services. The biggest problems were the lack of skills to operate in the network and difficulties in maintaining the network, weak communication, lack of confidence, inconvenient size or composition of the network, overlapping information, cliques, nodes and missing links. The benefits were versatile: knowledge and skills accumulate, the network multiplies resources, fluency of co-operation, innovations, commitment and trust increase, good practices expand, and moreover, the quality, many-sidedness and appropriateness of operations improve. Networking is beneficial but demanding. There are many limitations. Networks are not equal for every network partner (inequality in cost and benefit ratio). Networks produce social capital for participants. Successful networking requires trust relations between network partners.

Cooperative Behavior↗
PubMed1999

The Vermont Oxford Network: evidence-based quality improvement for neonatology.

The Vermont Oxford Network is a voluntary collaborative group of health professionals committed to improving the effectiveness and efficiency of medical care for newborn infants and their families through a coordinated program of research, education, and quality-improvement projects. In support of these activities, the Network maintains a clinical database of information about very low birth weight infants that now has more than 300 participating neonatal intensive care units (NICUs). We anticipate that these NICUs will submit data for 25 000 infants with birth weights of 401 to 1500 g born in 1998. The research program of the Network includes outcomes research and randomized clinical trials. The goal of Network outcomes research is to identify and explain the variations in clinical practice and patient outcomes that are apparent among NICUs. Network trials are designed to answer practical questions of importance to practitioners and families using pragmatic designs that can be integrated into the daily practice of neonatology. Quality improvement is a major focus of the Network. Members receive confidential quarterly and annual reports based on the Network database that document their performance and compare practices and outcomes at their unit with those at other units within the Network. These reports are intended to assist the members in identifying opportunities for improvement and to help them monitor the success of their improvement efforts. Although information is necessary for improvement to occur, it is not sufficient to foster lasting improvement by itself. Information must be translated into action. The Network is sponsoring an ongoing program of quality initiatives designed to provide members with the knowledge, skills, tools, and resources needed to foster action for improvement. The Network's first formal quality-improvement project, the NIC/Q Project, brought together 10 NICUs to apply the methods of collaborative improvement and benchmarking to neonatal intensive care. Building on the lessons learned in that initial project, the Network now is conducting the Vermont Oxford Network Evidence-Based Quality Improvement Collaborative for Neonatology, known as NIC/Q 2000. This 2-year collaborative will assist multidisciplinary teams from the 34 participating NICUs to develop four key habits for improvement: the habit for change, the habit for practice as a process, the habit for collaborative learning, and the habit for evidence-based practice. During the collaborative, participants will contribute to a knowledge bank of clinical, organizational, and operational change ideas for improving neonatal care. The coordinated program of research, education, and quality improvement described in this article is only possible because of the voluntary efforts of the members. The Network will continue to support these efforts by developing and providing improved tools and resources for the practice of evidence-based neonatology.neonatology, very low birth weight, database, network, quality improvement, evidence-based medicine, randomization, trials, outcomes, mortality, length of stay.

Adrenal Cortex Hormones↗
PubMed1996

Using artificial bat sonar neural networks for complex pattern recognition: recognizing faces and the speed of a moving target.

Two sets of studies examined the viability of using bat-like sonar input for artificial neural networks in complex pattern recognition tasks. In the first set of studies, a sonar neural network was required to perform two face recognition tasks. In the first task, the network was trained to recognize different faces regardless of facial expressions. Following training, the network was tested on its ability to generalize and correctly recognize faces using echoes of novel facial expressions that were not included in the training set. The neural network was able to recognize novel echoes of faces almost perfectly (above 96% accuracy) when it was required to recognize up to five faces. In the second face recognition task, a sonar neural network was trained to recognize the sex of 16 faces (eight males and eight females). After training, the network was able to correctly recognize novel echoes of those faces as 'male' or as 'female' faces with accuracy levels of 88%. However, the network was not able to recognize novel faces as 'male' or 'female' faces. In the second set of studies, a sonar neural network was required to learn to recognize the speed of a target that was moving towards the viewer. During training, the target was presented in a variety of orientations, and the network's performance was evaluated when the target was presented in novel orientations that were not included in the training set. The different orientations dramatically affected the amplitude and the frequency composition of the echoes. The neural network was able to learn and recognize the speed of a moving target, and to generalize to new orientations of the target. However, the network was not able to generalize to new speeds that were not included in the training set. The potential and limitations of using bat-like sonar as input for artifical neural networks are discussed.

Adult↗
PubMed2004

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

Action Potentials↗