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PubMed1997

An Incremental Adaptive Network for On-line Supervised Learning and Probability Estimation.

In this paper, a novel hybrid utilization of the Fuzzy ARTMAP (FAM) neural network and the Probabilistic Neural Network (PNN) is proposed for on-line learning and probability estimation tasks. There are two distinct advantages to the hybrid network. First, FAM is used as an underlying clustering algorithm to classify the input patterns into different recognition categories during the learning phase, resulting in a significant reduction in the number of pattern nodes required in the PNN. Second, a non-parametric posterior probability distribution estimation procedure, in accordance with the PNN paradigm (i.e. the Parzen-windows estimator), is employed during the prediction phase, where a probabilistic interpretation corresponding to Bayes decision theory can be provided for the predictions of FAM. In addition, several modifications are proposed to integrate both of the networks effectively into a unified platform for enhancing generalization. This hybrid approach also realizes an incremental learning system in which the necessity to specify a static network configuration a priori is eliminated as the network is able to "grow" to accommodate new input patterns sequentially and can thus operate in non-stationary environments. The performance of the network is evaluated with benchmark classification tasks and the results are compared with other approaches. Simulation results indicate that this hybrid network is capable of achieving a value near to the Bayes optimal classification rate. Copyright 1997 Elsevier Science Ltd.

Journal Article↗

PubMed2004

[Drug interaction computer programs: which choice?].

Adverse drug reactions represent a partly preventable cause of morbidity. Computer-based tools may be useful for the prevention of those events resulting from drug interactions. While several such tools are currently available to practitioners, they have rarely been compared regarding their performances and limits. This article presents a benchmark evaluation of seven drug interaction databases which may be elected by physicians or pharmacists as an aid to prescription on a regular basis. None of the databases tested appears to be perfect, and the systems vary in their overall completeness and ease of use.

Adverse Drug Reaction Reporting Systems↗

PubMed2003

SATCHMO: sequence alignment and tree construction using hidden Markov models.

MOTIVATION: Aligning multiple proteins based on sequence information alone is challenging if sequence identity is low or there is a significant degree of structural divergence. We present a novel algorithm (SATCHMO) that is designed to address this challenge. SATCHMO simultaneously constructs a tree and a set of multiple sequence alignments, one for each internal node of the tree. The alignment at a given node contains all sequences within its sub-tree, and predicts which positions in those sequences are alignable and which are not. Aligned regions therefore typically get shorter on a path from a leaf to the root as sequences diverge in structure. Current methods either regard all positions as alignable (e.g. ClustalW), or align only those positions believed to be homologous across all sequences (e.g. profile HMM methods); by contrast SATCHMO makes different predictions of alignable regions in different subgroups. SATCHMO generates profile hidden Markov models at each node; these are used to determine branching order, to align sequences and to predict structurally alignable regions. RESULTS: In experiments on the BAliBASE benchmark alignment database, SATCHMO is shown to perform comparably to ClustalW and the UCSC SAM HMM software. Results using SATCHMO to identify protein domains are demonstrated on potassium channels, with implications for the mechanism by which tumor necrosis factor alpha affects potassium current. AVAILABILITY: The software is available for download from http://www.drive5.com/lobster/index.htm

Algorithms↗

PubMed2006

Graemlin: general and robust alignment of multiple large interaction networks.

The recent proliferation of protein interaction networks has motivated research into network alignment: the cross-species comparison of conserved functional modules. Previous studies have laid the foundations for such comparisons and demonstrated their power on a select set of sparse interaction networks. Recently, however, new computational techniques have produced hundreds of predicted interaction networks with interconnection densities that push existing alignment algorithms to their limits. To find conserved functional modules in these new networks, we have developed Graemlin, the first algorithm capable of scalable multiple network alignment. Graemlin's explicit model of functional evolution allows both the generalization of existing alignment scoring schemes and the location of conserved network topologies other than protein complexes and metabolic pathways. To assess Graemlin's performance, we have developed the first quantitative benchmarks for network alignment, which allow comparisons of algorithms in terms of their ability to recapitulate the KEGG database of conserved functional modules. We find that Graemlin achieves substantial scalability gains over previous methods while improving sensitivity.

Algorithms↗

PubMed1998

Southampton University Hospitals NHS Trust: our approach to performance management.

Southampton University Hospitals Trust has developed a performance management strategy which is improving service quality, overcoming interdepartmental blockages and delivering significant savings with greater departmental ownership and commitment. The strategy also offers a solution to the age-old problem of if and when to market test by doing this only when it is apparent the inhouse team cannot deliver the required level of performance. This is achieved by agreeing the initiating key targets to reach or improve on high levels of performance using a variety of techniques including benchmarking, value management and external technical advice.

Cost Control↗

PubMedDate not supplied

Learning from the best leads to superior performance.

Xerox Corp., one of the earliest US proponents of benchmarking, has institutionalized the practice in its organization. This had lead to fundamental changes in how Xerox manages suppliers and develops products.

Economic Competition↗

PubMed2006

Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors.

Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of infinite) combination of Gaussian densities. This class includes, among others, the generalized Gaussian, the Jeffreys, and the Gaussian mixture priors. Necessary and sufficient conditions are stated under which the prior induced by a thresholding/shrinking denoising rule is a GSM. This result is then used to show that the prior induced by the "nonnegative garrote" thresholding/shrinking rule, herein termed the garrote prior, is a GSM. To compute the maximum a posteriori estimate, we propose a new generalized expectation maximization (GEM) algorithm, where the missing variables are the scale factors of the GSM densities. The maximization step of the underlying expectation maximization algorithm is replaced with a linear stationary second-order iterative method. The result is a GEM algorithm of O(N log N) computational complexity. In a series of benchmark tests, the proposed approach outperforms or performs similarly to state-of-the art methods, demanding comparable (in some cases, much less) computational complexity.

Algorithms↗

PubMed2004

A multiagent genetic algorithm for global numerical optimization.

In this paper, multiagent systems and genetic algorithms are integrated to form a new algorithm, multiagent genetic algorithm (MAGA), for solving the global numerical optimization problem. An agent in MAGA represents a candidate solution to the optimization problem in hand. All agents live in a latticelike environment, with each agent fixed on a lattice-point. In order to increase energies, they compete or cooperate with their neighbors, and they can also use knowledge. Making use of these agent-agent interactions, MAGA realizes the purpose of minimizing the objective function value. Theoretical analyzes show that MAGA converges to the global optimum. In the first part of the experiments, ten benchmark functions are used to test the performance of MAGA, and the scalability of MAGA along the problem dimension is studied with great care. The results show that MAGA achieves a good performance when the dimensions are increased from 20-10,000. Moreover, even when the dimensions are increased to as high as 10,000, MAGA still can find high quality solutions at a low computational cost. Therefore, MAGA has good scalability and is a competent algorithm for solving high dimensional optimization problems. To the best of our knowledge, no researchers have ever optimized the functions with 10,000 dimensions by means of evolution. In the second part of the experiments, MAGA is applied to a practical case, the approximation of linear systems, with a satisfactory result.

Journal Article↗

PubMed1995

A 10-point strategic checklist for rural health care systems.

A checklist format is used to provide a framework for rural hospital executives and community members for gauging the health and stability of rural hospitals and rural hospital systems. Benchmarks are provided for financial and operational performance and emphasis is placed on medical staff size and physician recruitment. Physician/hospital organizations and regional partnerships are used as examples of strategies available to rural providers. The importance of market knowledge and regional strategic alliances also is stressed. In an era of dwindling resources and tight reimbursement, rural providers are encouraged to consider cooperative clinical programming and technology consolidation.

Comprehensive Health Care↗

PubMed2000

Radical pruning: a method to construct skeleton radial basis function networks.

Trained radial basis function networks are well-suited for use in extracting rules and explanations because they contain a set of locally tuned units. However, for rule extraction to be useful, these networks must first be pruned to eliminate unnecessary weights. The pruning algorithm cannot search the network exhaustively because of the computational effort involved. It is shown that using multiple pruning methods with smart ordering of the pruning candidates, the number of weights in a radial basis function network can be reduced to a small fraction of the original number. The complexity of the pruning algorithm is quadratic (instead of exponential) in the number of network weights. Pruning performance is shown using a variety of benchmark problems from the University of California, Irvine machine learning database.

Algorithms↗

PubMed2006

Payoff-monotonic game dynamics and the maximum clique problem.

Evolutionary game-theoretic models and, in particular, the so-called replicator equations have recently proven to be remarkably effective at approximately solving the maximum clique and related problems. The approach is centered around a classic result from graph theory that formulates the maximum clique problem as a standard (continuous) quadratic program and exploits the dynamical properties of these models, which, under a certain symmetry assumption, possess a Lyapunov function. In this letter, we generalize previous work along these lines in several respects. We introduce a wide family of game-dynamic equations known as payoff-monotonic dynamics, of which replicator dynamics are a special instance, and show that they enjoy precisely the same dynamical properties as standard replicator equations. These properties make any member of this family a potential heuristic for solving standard quadratic programs and, in particular, the maximum clique problem. Extensive simulations, performed on random as well as DIMACS benchmark graphs, show that this class contains dynamics that are considerably faster than and at least as accurate as replicator equations. One problem associated with these models, however, relates to their inability to escape from poor local solutions. To overcome this drawback, we focus on a particular subclass of payoff-monotonic dynamics used to model the evolution of behavior via imitation processes and study the stability of their equilibria when a regularization parameter is allowed to take on negative values. A detailed analysis of these properties suggests a whole class of annealed imitation heuristics for the maximum clique problem, which are based on the idea of varying the parameter during the imitation optimization process in a principled way, so as to avoid unwanted inefficient solutions. Experiments show that the proposed annealing procedure does help to avoid poor local optima by initially driving the dynamics toward promising regions in state space. Furthermore, the models outperform state-of-the-art neural network algorithms for maximum clique, such as mean field annealing, and compare well with powerful continuous-based heuristics.

Journal Article↗

PubMed1998

Employing the zeta-transform to optimize the calculation of the synaptic conductance of NMDA and other synaptic channels in network simulations.

Calculation of the total conductance change induced by multiple synapses at a given membrane compartment remains one of the most time-consuming processes in biophysically realistic neural network simulations. Here we show that this calculation can be achieved in a highly efficient way even for multiply converging synapses with different delays by means of the zeta-transform. Using the example of an NMDA synapse, we show that every update of the total conductance is achieved by an iterative process requiring at most three recent multiplications, which together need only the history values from the two most recent iterations. A major advantage is that this small computational load is independent of the number of synapses simulated. A benchmark comparison to other techniques demonstrates superior performance of the zeta-transform. Nonvoltage-dependent synaptic channels can be treated similarly (Olshausen, 1990; Brettle & Niebur, 1994), and the technique can also be generalized to other synaptic channels.

Algorithms↗

PubMed1999

Noise-resilient estimation of optical flow by use of overlapped basis functions.

Conventional techniques for the computation of optical flow from image gradients are used to formulate the problem as a nonlinear optimization that comprises a gradient constraint term and a field smoothness factor. The results of these techniques are often erroneous, highly sensitive to noise and numerical precision, determined sparsely, and computationally expensive. We regularize the gradient constraint equation by modeling optical flow as a linear combination of a set of overlapped basis functions. We develop a theory for estimating model parameters robustly and reliably. We prove that the extended-least-squares solution proposed here is unbiased and robust to small perturbations in the gradient estimates and to mild deviations from the gradient constraint. Our solution is obtained with a numerically stable sparse matrix inversion, which gives a reliable flow-field estimate over the entire frame. To validate our claims, we perform a series of experiments on standard benchmark data sets at a range of noise levels. Overall, our algorithm outperforms by a wide margin the others considered in the comparison. We demonstrate the applicability of our algorithm to image mosaicking and to motion superresolution through experiments on noisy compressed sequences. We conclude that our flow-field model offers greater accuracy and robustness than conventional optical flow techniques in a variety of situations and permits real-time operation.

Algorithms↗

PubMed2001

Age- and gender-specific asthma death rates in patients taking long-acting beta2-agonists: prescription event monitoring pharmacosurveillance studies.

OBJECTIVE: Prescription event monitoring is a national drug safety surveillance scheme in which prescribers are prompted to report events and deaths following prescription of newly marketed drugs. This paper presents age- and gender-specific asthma death rates in patients prescribed the long-acting beta2-agonists salmeterol and bambuterol. DESIGN AND SETTING: Pharmacosurveillance cohort study of general practice patients in England. PATIENTS AND PARTICIPANTS: 15 406 patients prescribed salmeterol between December 1990 and May 1991, and 8098 patients prescribed bambuterol between February 1993 and December 1995. METHODS: Patients prescribed these drugs by general practitioners in England were identified using the national pharmacovigilance system of prescription event monitoring, in which details of all dispensed prescriptions were provided in confidence by the Prescription Pricing Authority. Questionnaires were sent to the prescriber asking for details of events occurring after the first prescription. In each study an attempt was made to establish the cause of all deaths reported on the questionnaires, via retrieval of the patients' medical notes or examination of death certificates. OUTCOME MEASURES AND RESULTS: There was little evidence of heterogeneity in the drug-specific death rates and we therefore present the combined age- and gender-specific death rates for the 2 cohorts. Overall, there were 85 asthma deaths among people taking the long-acting beta2-agonists studied (bambuterol and salmeterol cohorts combined). The overall death rate was 2.33 [95% confidence interval (CI) 1.84 to 2.84] per 10000 months of observation. There were 37 asthma deaths among male patients (rate 2.40 per 10000 months of observation; 95% CI 1.74 to 3.40) and 48 asthma deaths among female patients (rate 3.08 per 10000 months of observation; 95% CI 2.21 to 3.98). There was no difference in death rates when male and female patients were compared (rate ratio 0.78; 95% CI 0.49 to 1.22; p = 0.26). CONCLUSION: Prescription event monitoring is a form of prompted surveillance allowing rapid, uniform, national and practical assessment of newly marketed drugs on large cohorts of patients in England. These data provide benchmark rates from which to assess the performance of newly prescribed anti-asthma drugs and generate hypotheses for later analytical investigation in which confounding by indication and asthma severity can be controlled for. Any differences in these rates should be considered as a source of signal generation within the context of a surveillance programme, rather than as robust evidence of any mortality differential between drugs.

Adrenergic beta-Agonists↗

PubMed1998

New comparative performance study shows hospitals struggle with growing operating costs.

Data Benchmarks: Trouble keeping expenses in check? You're not alone. A newly-released study of hospital financial data shows hospitals nationwide continue their struggle to keep operating expenses down. Despite continued increases in median operating revenues at many hospitals, they are having a tough time keeping a lid on expenses in many categories. How does your organization compare? Here are the details.

Benchmarking↗

PubMed2003

Simultaneous sequence alignment and tree construction using hidden Markov models.

We present a new algorithm (SATCHMO) that simultaneously estimates a tree and generates a set of multiple sequence alignments given a set of protein sequences. Alignments are constructed for each node in the tree. These alignments predict the structurally conserved elements of the sequences in a subtree and are therefore of different lengths, and represent different amino acid preferences, at different nodes. Hidden Markov Models (HMMs) are also generated for each node and are used to determine branching order, to align sequences and to predict structurally alignable regions. In experiments on the BAliBASE benchmark alignment database, SATCHMO is shown to perform comparably to ClustalW and the UCSC SAM HMM software. Results using SATCHMO to identify protein domains are demonstrated on potassium channels, with implications for the mechanism by which tumor necrosis factor alpha affects potassium current.

Algorithms↗

PubMed2005

Software points out where performance needs improvement.

Quality improvement analysts are accustomed to running reports--lots of them--to identify areas of hospital activity that can use improvement. The work of calculating performance rates and comparing those to national benchmarks is time consuming and somewhat tedious. It doesn't have to be that way.

Efficiency, Organizational↗

PubMed1995

Understanding your hospital's true financial position and changing it.

Many hospital executives use operating margin as their primary measure of financial position in their hospitals. This article shows that return on equity (ROE) should be the primary test of financial performance for both taxable and tax-exempt hospitals. The ROE framework can be related to specific management actions that may improve performance through the strategic management model structure. Benchmarking data are employed to suggest successful management strategies.

Bankruptcy↗