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PubMed2001

Trauma outcome analysis of a Jakarta University Hospital using the TRISS method: validation and limitation in comparison with the major trauma outcome study. Trauma and Injury Severity Score.

BACKGROUND: In this prospective study, the TRISS methodology is used to compare trauma care at a University Hospital in Jakarta, Indonesia, with the standards reported in the Major Trauma Outcome Study (MTOS). METHODS: Between February 24, 1999, and July 1, 1999, all consecutive patients with multiple and severe trauma were included in the study (n = 105). Survival analysis was completed for 97 (92%) patients. RESULTS: The majority of patients were men (81%), and the average age was 28 years. Ninety-five patients (98%) sustained blunt trauma, with motor vehicle crashes being the most common (68%). The predicted mortality was 14% and the observed mortality was 29%. The Z and M statistics were 7.87 and 0.843, respectively. CONCLUSION: We conclude that in developing countries both institution-bound factors and specific limitations in the TRISS methodology are responsible for the difference between predicted and observed mortality, indicating the need for a regional database.

Adolescent↗
PubMed2001

Statistical mechanics of learning with soft margin classifiers.

We study the typical learning properties of the recently introduced soft margin classifiers (SMCs), learning realizable and unrealizable tasks, with the tools of statistical mechanics. We derive analytically the behavior of the learning curves in the regime of very large training sets. We obtain exponential and power laws for the decay of the generalization error towards the asymptotic value, depending on the task and on general characteristics of the distribution of stabilities of the patterns to be learned. The optimal learning curves of the SMCs, which give the minimal generalization error, are obtained by tuning the coefficient controlling the trade-off between the error and the regularization terms in the cost function. If the task is realizable by the SMC, the optimal performance is better than that of a hard margin support vector machine and is very close to that of a Bayesian classifier.

Algorithms↗
PubMed2005

In silico predictions of blood-brain barrier penetration: considerations to "keep in mind".

Within drug discovery, it is desirable to determine whether a compound will penetrate and distribute within the central nervous system (CNS) with the requisite pharmacokinetic and pharmacodynamic performance required for a CNS target or if it will be excluded from the CNS, wherein potential toxicities would mitigate its applicability. A variety of in vivo and in vitro methods for assessing CNS penetration have therefore been developed and applied to advancing drug candidates with the desired properties. In silico methods to predict CNS penetration from chemical structures have been developed to address virtual screening and prospective design. In silico predictive methods are impacted by the quality, quantity, sources, and generation of the measured data available for model development. Key considerations for predictions of CNS penetration include the comparison of local (in chemistry space) versus global (more structurally diverse) models and where in the drug discovery process such models may be best deployed. Preference should also be given to in vitro and in vivo measurements of greater mechanistic clarity that better support the development of structure-property relationships. Although there are numerous statistical methods that have been brought to bear on the prediction of CNS penetration, a greater concern is that such models are appropriate for the quality of measured data available and are statistically validated. In addition, the assessment of prediction uncertainty and relevance of predictive models to structures of interest are critical. This article will address these key considerations for the development and application of in silico methods in drug discovery.

Animals↗
PubMed2000

Automating parallel implementation of neural learning algorithms.

Neural learning algorithms generally involve a number of identical processing units, which are fully or partially connected, and involve an update function, such as a ramp, a sigmoid or a Gaussian function for instance. Some variations also exist, where units can be heterogeneous, or where an alternative update technique is employed, such as a pulse stream generator. Associated with connections are numerical values that must be adjusted using a learning rule, and and dictated by parameters that are learning rule specific, such as momentum, a learning rate, a temperature, amongst others. Usually, neural learning algorithms involve local updates, and a global interaction between units is often discouraged, except in instances where units are fully connected, or involve synchronous updates. In all of these instances, concurrency within a neural algorithm cannot be fully exploited without a suitable implementation strategy. A design scheme is described for translating a neural learning algorithm from inception to implementation on a parallel machine using PVM or MPI libraries, or onto programmable logic such as FPGAs. A designer must first describe the algorithm using a specialised Neural Language, from which a Petri net (PN) model is constructed automatically for verification, and building a performance model. The PN model can be used to study issues such as synchronisation points, resource sharing and concurrency within a learning rule. Specialised constructs are provided to enable a designer to express various aspects of a learning rule, such as the number and connectivity of neural nodes, the interconnection strategies, and information flows required by the learning algorithm. A scheduling and mapping strategy is then used to translate this PN model onto a multiprocessor template. We demonstrate our technique using a Kohonen and backpropagation learning rules, implemented on a loosely coupled workstation cluster, and a dedicated parallel machine, with PVM libraries.

Algorithms↗
PubMed2001

Neural cost functions and search strategies for the generation of block designs: an experimental evaluation.

A constraint satisfaction problem, namely the generation of Balanced Incomplete Block Designs (v, b, r, kappa, lambda)-BIBDs, is cast in terms of function optimization. A family of cost functions that both suit the problem and admit a neural implementation is defined. An experimental comparison spanning this repertoire of cost functions and three neural relaxation strategies (Down-Hill search, Simulated Annealing and a new Parallel Mean Search procedure), as applied to all BIBDs of up to 1000 entries, has been undertaken. The experiments were performed on a Connection Machine CM-200 and their analysis required a careful study of performance measures. The simplest cost function stood out as the best one for the three strategies. Parallel Mean Search, with several processors searching cooperatively in parallel, could solve a larger number of problems than the same number of processors working independently, but Simulated Annealing yielded overall the best results. Other conclusions, as detailed in the paper, could be drawn from the comparison, BIBDs remaining a challenging problem for neural optimization algorithms.

Algorithms↗
PubMedDate not supplied

Neural solution to the correction of miss distance in gun fire control system.

Multilayer perceptrons trained with the backpropagation algorithm are tested in gun fire control system for error correction and are compared to optimal algorithms based on minimum mean square error. The structure of the proposed neural controller is described and performance results are shown.

Algorithms↗
PubMed1999

Visual homing in analog hardware.

Insects of several species rely on visual landmarks for returning to important locations in their environment. The "average landmark vector model" is a parsimonious model which reproduces some aspects of the visual homing behavior of bees and ants. To gain insights in the structure and complexity of the neural apparatus that might underly the navigational capabilities of these animals, the average landmark vector model was implemented in analog hardware and used to control a mobile robot. The experiments demonstrate that the apparently complex task of visual homing might be realized by simple and mostly peripheral neural circuits in insect brains.

Animals↗
PubMed2000

Nonmonotonic generalization bias of Gaussian mixture models.

Theories of learning and generalization hold that the generalization bias, defined as the difference between the training error and the generalization error, increases on average with the number of adaptive parameters. This article, however, shows that this general tendency is violated for a gaussian mixture model. For temperatures just below the first symmetry breaking point, the effective number of adaptive parameters increases and the generalization bias decreases. We compute the dependence of the neural information criterion on temperature around the symmetry breaking. Our results are confirmed by numerical cross-validation experiments.

Computer Simulation↗
PubMed2000

Retrieval properties of a Hopfield model with random asymmetric interactions.

The process of pattern retrieval in a Hopfield model in which a random antisymmetric component is added to the otherwise symmetric synaptic matrix is studied by computer simulations. The introduction of the anti-symmetric component is found to increase the fraction of random inputs that converge to the memory states. However, the size of the basin of attraction of a memory state does not show any significant change when asymmetry is introduced in the synaptic matrix. We show that this is due to the fact that the spurious fixed points, which are destabilized by the introduction of asymmetry, have very small basins of attraction. The convergence time to spurious fixed-point attractors increases faster than that for the memory states as the asymmetry parameter is increased. The possibility of convergence to spurious fixed points is greatly reduced if a suitable upper limit is set for the convergence time. This prescription works better if the synaptic matrix has an antisymmetric component.

Algorithms↗
PubMed2004

Geometrical computations explain projection patterns of long-range horizontal connections in visual cortex.

Neurons in primary visual cortex respond selectively to oriented stimuli such as edges and lines. The long-range horizontal connections between them are thought to facilitate contour integration. While many physiological and psychophysical findings suggest that collinear or association field models of good continuation dictate particular projection patterns of horizontal connections to guide this integration process, significant evidence of interactions inconsistent with these hypotheses is accumulating. We first show that natural random variations around the collinear and association field models cannot account for these inconsistencies, a fact that motivates the search for more principled explanations. We then develop a model of long-range projection fields that formalizes good continuation based on differential geometry. The analysis implicates curvature(s) in a fundamental way, and the resulting model explains both consistent data and apparent outliers. It quantitatively predicts the (typically ignored) spread in projection distribution, its nonmonotonic variance, and the differences found among individual neurons. Surprisingly, and for the first time, this model also indicates that texture (and shading) continuation can serve as alternative and complementary functional explanations to contour integration. Because current anatomical data support both (curve and texture) integration models equally and because both are important computationally, new testable predictions are derived to allow their differentiation and identification.

Animals↗
PubMed2001

A study of the simulated evolution of the spectral sensitivity of visual agent receptors.

In this article we study a model for the evolution of the spectral sensitivity of visual receptors for agents in a continuous virtual environment. The model uses a genetic algorithm (GA) to evolve the agent sensors along with the control of the agents by requiring the agents to solve certain tasks in the simulation environment. The properties of the evolved sensors are analyzed for different scenarios. In particular, it is shown that the GA is able to find a balance between sensor costs and agent performance in such a way that the spectral sensor sensitivity reflects the emission spectrum of the target objects and that the capability of the sensors to evolve can help the agents significantly in adapting to their task.

Algorithms↗
PubMed2001

Situational descriptions of behavioral procedures: the in situ testbed.

We demonstrate the In Situ testbed, a system that aids in evaluating computational models of learning, including artificial neural networks. The testbed models contingencies of reinforcement rising an extension of Mechner's (1959) notational system for the description of behavioral procedures. These contingencies are input to the model under test. The model's output is displayed as cumulative records. The cumulative record can then be compared to one produced by a pigeon exposed to the same contingencies. The testbed is tried with three published models of learning. Each model is exposed to up to three reinforcement schedules (testing ends when the model does not produce acceptable cumulative records): continuous reinforcement and extinction, fixed ratio, and fixed interval. The In Sitt testbed appears to be a reliable and valid testing procedure for comparing models of learning.

Animals↗
PubMedDate not supplied

Data mining and healthcare informatics.

OBJECTIVE: To acquaint members of the Academy with a relatively recent development in the area of data exploration and statistical analysis. METHODS: A review of the concepts and methods inherent in data-mining with a special emphasis on the those methods applicable to predictive modeling. RESULTS: Data-mining is demonstrated to be a useful tool for researchers in those circumstances where large amounts of information are available. CONCLUSIONS: With the advent and proliferation of on-line data collection, truly massive databases are now available to health care researchers. In that situation, data-mining methods yield some unique opportunities to researchers who wish to develop prediction models and to establish associations.

Database Management Systems↗