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At least 973 records · Page 54Linked to original sources
PubMed1997

Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development.

Artificial neural networks (ANN) methodology is a new modeling method that has not been broadly applied to pharmaceutical sciences up to now. The aim of this paper is to give a detailed description of the associating networks as well as a description of less well-known networks (i.e., feature-extracting and nonadaptive networks) and their scope of application in pharmaceutical sciences. The descriptions include the historical origin and the basic concepts behind the computing. ANN are based on the attempt to model the neural networks of the brain. Learning algorithms for associating ANN use mathematical procedures usually derived from the gradient descent method whereas feature-extracting ANN map multidimensional input data sets onto two-dimensional spaces. Nonadaptive ANN map data sets and are able to reconstruct their patterns when presented with corrupted or noisy samples. Associating networks can typically be applied in the pharmaceutical field as an alternative to traditional response surface methodology, feature-extracting networks as alternative to principal component analysis, and nonadaptive networks for image recognition. Based on these abilities, the potential application fields of the ANN methodology in the pharmaceutical sciences is broad, ranging from clinical pharmacy through biopharmacy, drug and dosage form design, to interpretation of analytical data. The few applications presented in the pharmaceutical technology area seem promising and should be investigated in more detail.

Algorithms↗
PubMed1999

Prediction of oculocardiac reflex in strabismus surgery using neural networks.

Successfully predicting an oculocardiac reflex (OCR) is difficult to achieve despite various proposed maneuvers. The aim of this study was to test the models built up by neural networks to predict the occurrence of OCR during strabismus surgery in children. Premedication was not given. Atropine 0.01 mg/kg was medicated just before induction. Induction was performed with fentanyl or ketorolac, followed by propofol. Atracurium or vecuronium was given for intubation. Anesthesia was maintained with O2-N2O with continuous propofol infusion. Chi-square test was performed for induction agents, gender, weight, muscle blockade, repaired muscle, number of repaired muscles, duration of operation to detect any association between the occurrence of OCR and to develop the model of neural networks. The multi-layer perceptron, radial basis function and Bayesian backpropagation network were tested. The occurrence of OCR was significantly associated with gender and repaired muscle (p < 0.05). Gender, repaired muscle and age were considered as input for the multi-layer perceptron, radial basis function and Bayesian backpropagation network. Three neural networks had predicted the same correction rate in the occurrence of OCR as being 87.5% overall among 16 patients' records tested. These models are conceptually different in predicting compared to conventional maneuvers, and have the advantage of testing individually and foretelling the propensity. By comparison neural networks use grouped experiential data and predict OCR by the learning rule. Neural networks require a relatively abundant number of experienced and homogenous patients' records to establish an accurate model. The multi-layer perceptron, radial basis function and Bayesian backpropagation modeling network may be an alternative way, and preferable to vagal tone maneuvers if the associated relationships to the occurrence of OCR are more clearly defined.

Adolescent↗
PubMed1996

Artificial neural network prediction of ascites in broilers.

An artificial neural network was trained to predict the presence or absence of ascites in broiler chickens. The neural network was a three-layer back-propagation neural network with an input layer of 15 neurons (defining 15 physiological variables), a hidden layer of 16 neurons, and an output layer of 2 neurons (the presence or absence of ascites). Male by-products of a breeder pullet line were brooded at 32 and 30 C during Weeks 1 and 2, respectively. The training set for the neural network consisted of data from birds subjected to cool temperatures (18 C) to induce ascites. After training, the predictive ability of the neural network was verified with two new data sets. The second data set was from birds subjected to cool temperatures (18 C). The third data set was from birds subjected to clamping of the pulmonary artery to simulate the physiological processes involved in ascites (the temperature was 24 C). A comparison was made between laboratory diagnostic results and the neural network predicted ascites incidence. The neural network accurately identified the presence or absence of ascites in the first (training) set. Two false positives and one false positive were identified in the second and third verification sets, respectively. The birds identified as false positives were determined to be in the developmental stages of ascites before the occurrence of fluid accumulation. Artificial neural networks were found to effectively identify broilers with and without ascites.

Animals↗
PubMed1999

Neural networks for visual field analysis: how do they compare with other algorithms?

PURPOSE: To compare the performance of a neural network in identifying visual field defects with the performance of other available algorithms. METHODS: A feed-forward neural network with a single hidden layer was trained to recognize visual field defects previously collected in a longitudinal follow-up glaucoma study, and then tested on fields taken from the same study but not used in the training. The receiver operating characteristics of the network then were compared with the previously determined performance of other algorithms on the same data set. RESULTS: At a specificity greater than 90%, the neural network was more sensitive than any of the available algorithms (although only the global indices were available for comparison, as the cluster and cross-meridional algorithms did not achieve such high specificity at their current settings). At a lower specificity (80-85%), the neural network was unable to attain the high sensitivity of the cluster or cross-meridional algorithms; in fact, the cluster algorithm from the Low-Tension Glaucoma study was significantly more sensitive. CONCLUSION: The receiver operating characteristics of a feed-forward neural network designed to detect visual field defects were explored. At a very high specificity (90-95%) a neural network performed better than the global indices. However, at a lower specificity (78%-88%), the neural network performed worse than cluster and cross-meridional algorithms.

Algorithms↗
PubMedDate not supplied

The competitive dynamics of network-based businesses.

Telecommunications carriers, transportation companies, and banks are among the many network-based businesses--companies that move people, goods, or information from various points to various other points. Managers have long assumed that customers valued all links in these networks equally. It was thought that banking customers, for example, sought access to all of the branches throughout the network or that shipping customers wanted to be able to send packages everywhere. Intuitively, managers thought that many of their customers' needs were, in reality, narrower, but they had no way of knowing which links were most important. New computing power and robust mapping software now make it possible to understand network customers better. In applying this technology, the authors, both consultants from McKinsey & Company, have uncovered three distinct usage patterns: one in which all links are, indeed, valued equally; another in which customers concentrate their use in particular zones; and a third in which customers value only individual links. Each of these patterns requires a different strategy to direct executives in making the decisions fundamental to managing any network-based business: whether to open or close outlets, whether to connect their network to others, and how to organize business units so that they reflect the network's structure. Those who don't spot the patterns or understand their strategic implications will find themselves on the losing end of the network battle.

Capital Financing↗
PubMed2000

Generalized and partial synchronization of coupled neural networks.

Synchronization of neural signals has been proposed as a temporal coding scheme representing cooperated computation in distributed cortical networks. Previous theoretical studies in that direction mainly focused on the synchronization of coupled oscillatory subsystems and neglected more complex dynamical modes, that already exist on the single-unit level. In this paper we study the parametrized time-discrete dynamics of two coupled recurrent networks of graded neurons. Conditions for the existence of partially synchronized dynamics of these systems are derived, referring to a situation where only subsets of neurons in each sub-network are synchronous. The coupled networks can have different architectures and even a different number of neurons. Periodic as well as quasiperiodic and chaotic attractors constrained to a manifold M of synchronized components are observed. Examples are discussed for coupled 3-neuron networks having different architectures, and for coupled 2-neuron and 3-neuron networks. Partial synchronization of different degrees is demonstrated by numerical results for selected sets of parameters. In conclusion, the results show that synchronization phenomena far beyond completely synchronized oscillations can occur even in simple coupled networks. The type of the synchronization depends in an intricate way on stimuli, history and connectivity as well as other parameters of the network. Specific inputs can further switch between different operational modes in a complex way, suggesting a similarly rich spatio-temporal behaviour in real neural systems.

Neural Networks, Computer↗
PubMed2000

Isolated directional preponderance of caloric nystagmus: II. A neural network model.

HYPOTHESIS: The purpose of this study was to simulate an isolated directional preponderance (DP) on bithermal caloric testing by constructing a realistic neural network model. The simulation was designed to capture not only the characteristics of the nystagmus response to caloric stimulation but also the response to rotational stimulation in patients with an isolated caloric DP. BACKGROUND: The nature of an isolated DP--that is, a DP in the absence of a significant spontaneous nystagmus or canal paresis--is outlined in the preceding article. In this article, the authors investigate the possible neural basis for an isolated caloric DP using the mathematic modeling technique of neural network simulation. Neural network models are typically abstract in nature; however, in this case the network was based on the known structure and function of the central vestibular system. METHODS: The neural network model was based on the known neuroanatomy and neurophysiology of the horizontal vestibuloocular reflex pathway. A leftward-rightward asymmetric modification of the dynamic responses of simulated medial vestibular nucleus type IA neurons on one side, or of type 2 neurons on the other side, to peripheral input would generate an isolated caloric DP. RESULTS: The values of DP and associated canal paresis produced by the network were within the same range as in the patient group. The network also predicted that the rotational DP would be lower than the caloric DP: between 2.5% and 56.9% of the caloric DP value. The actual rotational DP value was between 3% and 57% (average 41%) of the corresponding caloric DP value. CONCLUSIONS: An isolated caloric DP can be simulated by a neural network model by modifying the activity of model units that represent medial vestibular nucleus neurons. An asymmetric dynamic response by a gain-enhancement function of either type 1A neurons on one side or of type 2 neurons on the other was sufficient to produce an isolated caloric DP. Excitatory gain enhancement of type 2 neurons produced a smaller rotational DP than a similar modification of type 1 neurons. This result indicates a potential neural locus for the generation of an isolated DP in patients with vestibular disorders.

Caloric Tests↗
PubMed2003

A project plan for construction and cabling of picture archiving and communication system network.

OBJECTIVE: To determine a network solution to meet the network requirements of the heavy data flow, load balance, and potential network storms from expansion of picture archiving and communication system (PACS) application. METHODS: Intel Netstructure 480T Giga Switch was used as the main switch and connected to each building by fiber channel at 1 Giga speed to archive 100 MB/s to each port. At the same time, the in-dependence of the original network construction was physically kept. The layer 3 and 4 switchers were used as load balance to reduce the heavy load of the network, and all the cabling for PACS used the super CAT5 along with the Intel NetStructure 1520 to prepare for potential network storms. RESULTS: An advanced intranet was set up to fully meet the high standard requirement of the PACS. The foundation for upgrading the whole network system to 1 Giga application was built to achieve sharing and transmission of images, information, and patient data within the hospital. The base was established for the standardized management of the hospital. CONCLUSION: Good planning is the first step in setting up PACS, and the equipment forms the necessary platform to run PACS and all kinds of hospital information system (HIS). The networking construction is the foundation of e-hospital.

Computer Communication Networks↗
PubMed1992

The application of backpropagation neural networks to problems in pathology and laboratory medicine.

Neural networks are a group of computer-based pattern recognition technologies that have been applied to problems in clinical diagnosis. This review focuses on one member of the group of neural networks, the backpropagation network. The steps in creating a backpropagation network are (1) collecting adequate training facts, (2) choosing the specific network structure, (3) training the network, and (4) cross-validating the trained network. The first published applications of backpropagation networks to problems in pathology and laboratory medicine have appeared recently. These applications are in the areas of image analysis and interpretation of laboratory results, and they demonstrate the feasibility of the approach.

Evaluation Studies as Topic↗
PubMed2004

Body area network--a key infrastructure element for patient-centered telemedicine.

The Body Area Network (BAN) extends the range of existing wireless network technologies by an ultra-low range, ultra-low power network solution optimised for long-term or continuous healthcare applications. It enables wireless radio communication between several miniaturised, intelligent Body Sensor (or actor) Units (BSU) and a single Body Central Unit (BCU) worn at the human body. A separate wireless transmission link from the BCU to a network access point--using different technology--provides for online access to BAN components via usual network infrastructure. The BAN network protocol maintains dynamic ad-hoc network configuration scenarios and co-existence of multiple networks.BAN is expected to become a basic infrastructure element for electronic health services: By integrating patient-attached sensors and mobile actor units, distributed information and data processing systems, the range of medical workflow can be extended to include applications like wireless multi-parameter patient monitoring and therapy support. Beyond clinical use and professional disease management environments, private personal health assistance scenarios (without financial reimbursement by health agencies / insurance companies) enable a wide range of applications and services in future pervasive computing and networking environments.

Artificial Intelligence↗
PubMed2005

The Prevention Research Centers Healthy Aging Research Network.

BACKGROUND: The Prevention Research Centers Healthy Aging Research Network (PRC-HAN), funded by the Centers for Disease Control and Prevention's (CDC's) Healthy Aging program, was created in 2001 to help develop partnerships and create a research agenda that promotes healthy aging. The nine universities that participate in the network use their expertise in aging research to collaborate with their communities and other partners to develop and implement health promotion interventions for older adults at the individual, organizational, environmental, and policy levels. CONTEXT: The population of older adults in the United States is growing rapidly; approximately 20% of Americans will be aged 65 years or older by 2030. The health and economic impact of an aging society compel the CDC and the public health community to place increased emphasis on preventing unnecessary disease, disability, and injury among older Americans. METHODS: The PRC-HAN has a broad research agenda that addresses health-promoting skills and behaviors, disease and syndrome topics, and knowledge domains. The network chose physical activity for older adults as its initial focus for research and has initiated two networkwide projects: a comprehensive, multisite survey that collected information on the capacity, content, and accessibility of physical activity programs for older adults and a peer-reviewed publication that describes the role of public health in promoting physical activity among older adults. In addition to participating in the core research area, each network member works independently with its community committee on PRC-HAN activities. CONSEQUENCES: As a result, the network is 1) expanding prevention research for older adults and their communities; 2) promoting the translation and dissemination of findings to key stakeholders; 3) strengthening PRC-HAN capacity through partnerships and expanded funding; and 4) stimulating the adoption of policies and programs by engaging policymakers, planners, and practitioners. In 2003, the PRC-HAN initiated an internal evaluation to better define the network's contributions to healthy aging, formalize internal processes, and better equip itself to serve as a model for other PRC thematic networks. The PRC-HAN is conducting a pilot evaluation for eventual inclusion in the PRC national evaluation. INTERPRETATION: The PRC-HAN has established itself as an effective research network to promote healthy aging. It has developed trust and mutual respect among participants, forged strong ties to local communities, and shown the ability to combine its expertise in healthy aging with that of partners in national, state, and local organizations.

Aged↗
PubMed1992

Application of neural networks to the interpretation of laboratory data in cancer diagnosis.

Neural networks are a relatively new method of multivariate analysis. The purpose of this study was to investigate the ability of neural networks to differentiate benign from malignant breast conditions on the basis of the pattern of nine variables: patient age, total cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein A-I, apolipoprotein B, albumin, the tumor marker CA15-3, and the Fossel index (measurement of methylene and methyl line-widths in proton NMR spectra). The laboratory analyses were made with blood plasma or serum specimens. The neural network was "trained" with 57 patients: 23 patients with breast malignancies and 34 patients with benign breast conditions. A neural network with nine input neurons, 15 hidden neurons, and two output neurons correctly classified all 57 patients. The ability of the network to predict the diagnoses of patients that it had no encountered in training was tested with a separate group (cross-validation group) of 20 patients. The network correctly predicted the diagnoses for 80% of these patients. For comparison we analyzed the same sets of 57 training patients and 20 cross-validation patients by quadratic discriminant function analysis. The quadratic discriminant function, calculated from the same 57 patients used to train the neural network, correctly classified 84% of the 57 patients, and correctly diagnosed 75% of the 20 cross-validation patients. The results suggest that neural networks are a potentially useful multivariate method for optimizing the diagnostic utility of laboratory data.

Antigens, Tumor-Associated, Carbohydrate↗
PubMedDate not supplied

Potential usefulness of an artificial neural network for assessing ventricular size.

An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degrees of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to constant over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles.

Cerebral Ventricles↗
PubMed1994

Experimental analysis of large belief networks for medical diagnosis.

We present an experimental analysis of two parameters that are important in knowledge engineering for large belief networks. We conducted the experiments on a network derived from the Internist-1 medical knowledge base. In this network, a generalization of the noisy-OR gate is used to model causal independence for the multivalued variables, and leak probabilities are used to represent the nonspecified causes of intermediate states and findings. We study two network parameters, (1) the parameter governing the assignment of probability values to the network, and (2) the parameter denoting whether the network nodes represent variables with two or more than two values. The experimental results demonstrate that the binary simplification computes diagnoses with similar accuracy to the full multivalued network. We discuss the implications of these parameters, as well other network parameters, for knowledge engineering for medical applications.

Artificial Intelligence↗
PubMed1993

A spiking network model of short-term active memory.

Studies of cortical neurons in monkeys performing short-term memory tasks have shown that information about a stimulus can be maintained by persistent neuron firing for periods of many seconds after removal of the stimulus. The mechanism by which this sustained activity is initiated and maintained is unknown. In this article we present a spiking neural network model of short-term memory and use it to investigate the hypothesis that recurrent, or "re-entrant," networks with constant connection strengths are sufficient to store graded information temporarily. The synaptic weights that enable the network to mimic the input-output characteristics of an active memory module are computed using an optimization procedure for recurrent networks with non-spiking neurons. This network is then transformed into one with spiking neurons by interpreting the continuous output values of the nonspiking model neurons as spiking probabilities. The behavior of the model neurons in this spiking network is compared with that of 179 single units previously recorded in monkey inferotemporal (IT) cortex during the performance of a short-term memory task. The spiking patterns of almost every model neuron are found to resemble closely those of IT neurons. About 40% of the IT neuron firing patterns are also found to be of the same types as those of model neurons. A property of the spiking model is that the neurons cannot maintain precise graded activity levels indefinitely, but eventually relax to one of a few constant activities called fixed-point attractors. The noise introduced into the model by the randomness of spiking causes the network to jump between these attractors. This switching between attractor states generates spike trains with a characteristic statistical temporal structure. We found evidence for the same kind of structure in the spike trains from about half of the IT neurons in our test set. These results show that the behavior of many real cortical memory neurons is consistent with an active storage mechanism based on recurrent activity in networks with fixed synaptic strengths.

Action Potentials↗
PubMed1993

A genetic algorithm to improve a neural network to predict a patient's response to warfarin.

The ability of neural networks to predict the international normalised ratio (INR) for patients treated with Warfarin was investigated. Neural networks were obtained by using all the predictor variables in the neural network, or by using a genetic algorithm to select an optimal subset of predictor variables in a neural network. The use of a genetic algorithm gave a marked and significant improvement in the prediction of the INR in two of the three cases investigated. The mean error in these cases, typically, reduced from 1.02 +/- 0.29 to 0.28 +/- 0.25 (paired t-test, t = -4.71, p < 0.001, n = 30). The use of a genetic algorithm with Warfarin data offers a significant enhancement of the predictive ability of a neural network with Warfarin data, identifies significant predictor variables, reduces the size of the neural network and thus the speed at which the reduced network can be trained, and reduces the sensitivity of a network to over-training.

Algorithms↗
PubMed1997

Automated interpretation of myocardial SPECT perfusion images using artificial neural networks.

UNLABELLED: The purpose of this study was to develop a computer-based method for automatic detection and localization of coronary artery disease (CAD) in myocardial bull's-eye scintigrams. METHODS: A population of 135 patients who had undergone both myocardial 99mTc-sestamibi rest-stress scintigraphy and coronary angiography within 3 mo was studied. Different image data reduction methods, including pixel averaging and two-dimensional Fourier transform, were applied to the bull's-eye scintigrams. After a quantitative and qualitative evaluation of these methods, 30 Fourier components were chosen as inputs to multilayer perceptron artificial neural networks. The networks were trained to detect CAD in two vascular territories, using coronary angiography as gold standard. A "leave one out" procedure was used for training and evaluation. The performance of the networks was compared to those of two human experts. RESULTS: One of the human experts detected CAD in one of two vascular territories, with a sensitivity of 54.4% at a specificity of 70.5%. The sensitivity of the networks was significantly higher at that level of specificity (77.2%, p = 0.0022). The other expert had a sensitivity of 63.2% at a specificity of 61.5%. The networks had a sensitivity of 77.2% (p = 0.038) at this specificity level as well. The differences in sensitivity between human experts and networks for the other vascular territory were all less than 6% and were not statistically significant. CONCLUSION: Artificial neural networks can detect CAD in myocardial bull's-eye scintigrams with such a high accuracy that the application of neural networks as clinical decision support tools appears to have significant potential.

Coronary Angiography↗
PubMed1998

The network approach and interventions to prevent HIV among injection drug users.

OBJECTIVE: To review human immunodeficiency virus (HIV) risk reduction interventions among injecting drug users (IDUs) that have adopted a network approach. METHODS: The design and outcomes of selected network-based interventions among IDUs are reviewed using the network concepts of the dyad (two-person relationship), the personal risk network (an index person and all of his or her relationship), and the "sociometric" network (the complete set of relations between people in a population) and community. RESULTS: In a dyad intervention among HIV-serodiscordant couples, many of which included IDUs, there were no HIV seroconversions. Participants in personal risk network interventions were more likely to reduce drug risks and in some of these interventions, sexual risks, than were participants in individual-based interventions. Sociometric network interventions reached more IDUs and may be more cost-effective than individual-based interventions. CONCLUSION: Network-based HIV risk reduction interventions among IDUs, and others at risk for HIV, hold promise and should be encouraged.

Community Networks↗