SEARCH · PubMed Health

Results for “Model”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32Linked to original sources

PubMed2000

Exact calculation of the spatiotemporal correlations in the takayasu model and in the q model of force fluctuations in bead packs

We calculate exactly the two point mass-mass correlations in arbitrary spatial dimensions in the aggregation model of Takayasu. In this model, masses diffuse on a lattice, coalesce upon contact, and adsorb unit mass from outside at a constant rate. Our exact calculation of the variance of mass at a given site proves explicitly, without making any assumption of scaling, that the upper critical dimension of the model is 2. We also extend our method to calculate the spatiotemporal correlations in a generalized class of models with aggregation, fragmentation, and injection, which include, in particular, the q model of force fluctuations in bead packs. We present explicit expressions for the spatiotemporal force-force correlation function in the q model. These can be used to test the applicability of the q model in experiments.

Journal Article↗

PubMed2001

Macroscopic traffic models from microscopic car-following models.

We present a method to derive macroscopic fluid-dynamic models from microscopic car-following models via a coarse-graining procedure. The method is first demonstrated for the optimal velocity model. The derived macroscopic model consists of a conservation equation and a momentum equation, and the latter contains a relaxation term, an anticipation term, and a diffusion term. Properties of the resulting macroscopic model are compared with those of the optimal velocity model through numerical simulations, and reasonable agreement is found although there are deviations in the quantitative level. The derivation is also extended to general car-following models.

Journal Article↗

PubMed2004

Epsilon-insensitive fuzzy c-regression models: introduction to epsilon-insensitive fuzzy modeling.

This paper introduces a new epsilon-insensitive fuzzy c-regression models (epsilonFCRM), that can be used in fuzzy modeling. To fit these regression models to real data, a weighted epsilon-insensitive loss function is used. The proposed method make it possible to exclude an intrinsic inconsistency of fuzzy modeling, where crisp loss function (usually quadratic) is used to match real data and the fuzzy model. The epsilon-insensitive fuzzy modeling is based on human thinking and learning. This method allows easy control of generalization ability and outliers robustness. This approach leads to c simultaneous quadratic programming problems with bound constraints and one linear equality constraint. To solve this problem, computationally efficient numerical method, called incremental learning, is proposed. Finally, examples are given to demonstrate the validity of introduced approach to fuzzy modeling.

Journal Article↗

PubMed1992

Extension of the Limit Cycle Reciprocal Interaction Model of REM cycle control. An integrated sleep control model.

The Limit Cycle Reciprocal Interaction Model (LCRIM) describes the stable interaction of REM-promoting and REM-antagonizing neurons which generates the periodic Occurrence of REM sleep. The cycling of this physiological oscillator is subject to perturbation from excitatory input, referred to as E, which derives from other parts of the nervous system. An extended model is now proposed which combines the ultraradian REM regulation modelled by the LCRIM with the homeostatic regulation of slow-wave activity modelled by the Two-Process Models of Borbély, Achermann and Beersma. In addition, this integrated model extends the E construct to relate explicitly to both the REM and slow-wave sleep control systems, and to have stochastic dynamics. Overall, the model generates qualitatively realistic SWA, REM and wake-up patterns. However, its performance awaits quantitative validation.

Journal Article↗

PubMedDate not supplied

Common data model for neuroscience data and data model exchange.

OBJECTIVE: Generalizing the data models underlying two prototype neurophysiology databases, the authors describe and propose the Common Data Model (CDM) as a framework for federating a broad spectrum of disparate neuroscience information resources. DESIGN: Each component of the CDM derives from one of five superclasses-data, site, method, model, and reference-or from relations defined between them. A hierarchic attribute-value scheme for metadata enables interoperability with variable tree depth to serve specific intra- or broad inter-domain queries. To mediate data exchange between disparate systems, the authors propose a set of XML-derived schema for describing not only data sets but data models. These include biophysical description markup language (BDML), which mediates interoperability between data resources by providing a meta-description for the CDM. RESULTS: The set of superclasses potentially spans data needs of contemporary neuroscience. Data elements abstracted from neurophysiology time series and histogram data represent data sets that differ in dimension and concordance. Site elements transcend neurons to describe subcellular compartments, circuits, regions, or slices; non-neuroanatomic sites include sequences to patients. Methods and models are highly domain-dependent. CONCLUSIONS: True federation of data resources requires explicit public description, in a metalanguage, of the contents, query methods, data formats, and data models of each data resource. Any data model that can be derived from the defined superclasses is potentially conformant and interoperability can be enabled by recognition of BDML-described compatibilities. Such metadescriptions can buffer technologic changes.

Animals↗

PubMed1999

Empirical versus mechanistic modelling: comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates.

The aim of the current study was to compare the predictive performance of a mechanistically based model and an empirical artificial neural network (ANN) model to describe the relationship between the tissue-to-unbound plasma concentration ratios (Kpu's) of 14 rat tissues and the lipophilicity (LogP) of a series of nine 5-n-alkyl-5-ethyl barbituric acids. The mechanistic model comprised the water content, binding capacity, number of the binding sites, and binding association constant of each tissue. A backpropagation ANN with 2 hidden layers (33 neurons in the first layer, 9 neurons in the second) was used for the comparison. The network was trained by an algorithm with adaptive momentum and learning rate, programmed using the ANN Toolbox of MATLAB. The predictive performance of both models was evaluated using a leave-one-out procedure and computation of both the mean prediction error (ME, showing the prediction bias) and the mean squared prediction error (MSE, showing the prediction accuracy). The ME of the mechanistic model was 18% (range, 20 to 57%), indicating a tendency for overprediction; the MSE is 32% (range, 6 to 104%). The ANN had almost no bias: the ME was 2% (range, 36 to 64%) and had greater precision than the mechanistic model, MSE 18% (range, 4 to 70%). Generally, neither model appeared to be a significantly better predictor of the Kpu's in the rat.

Algorithms↗

PubMed2004

Systematic errors inherent in the current modeling of the reflected downward flux term used by remote sensing models.

An underlying assumption of satellite data assimilation systems is that the radiative transfer model used to simulate observed satellite radiances has no errors. For practical reasons a fast-forward radiative transfer model is used instead of a highly accurate line-by-line model. The fast model usually replaces the spectral integration of spectral quantities with their monochromatic equivalents, and the errors due to these approximations are assumed to be negligible. The reflected downward flux term contains many approximations of this nature, which are shown to introduce systematic errors. In addition, many fast-forward radiative transfer models simulate the downward flux as the downward radiance along a path defined by the secant of the mean emergent angle, the diffusivity factor. The diffusivity factor is commonly set to 1.66 or to the secant of the satellite zenith angle. Neither case takes into account that the diffusivity factor varies with optical depth, which introduces further errors. I review the two most commonly used methods for simulating reflected downward flux by fast-forward radiative transfer models and point out their inadequacies and limitations. An alternate method of simulating the reflected downward flux is proposed. This method transforms the surface-to-satellite transmittance profile to a transmittance profile suitable for simulating the reflected downward flux by raising the former transmittance to the power of kappa, where kappa itself is a function of channel, surface pressure, and satellite zenith angle. It is demonstrated that this method reduces the fast-forward model error for low to moderate reflectivities.

Journal Article↗

PubMedDate not supplied

Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil.

Most ecologic studies use geographical areas as units of observation. Because data from areas close to one another tend to be more alike than those from distant areas, estimation of effect size and confidence intervals should consider spatial autocorrelation of measurements. In this report we demonstrate a method for modeling spatial autocorrelation within a mixed model framework, using data on environmental and socioeconomic determinants of the incidence of visceral leishmaniasis (VL) in the city of Teresina, Piauí, Brazil. A model with a spherical covariance structure indicated significant spatial autocorrelation in the data and yielded a better fit than one assuming independent observations. While both models showed a positive association between VL incidence and residence in a favela (slum) or in areas with green vegetation, values for the fixed effects and standard errors differed substantially between the models. Exploration of the data's spatial correlation structure through the semivariogram should precede the use of these models. Our findings support the hypothesis of spatial dependence of VL rates and indicate that it might be useful to model spatial correlation in order to obtain more accurate point and standard error estimates.

Analysis of Variance↗

PubMed2000

Modeling therapeutic strategies in rheumatoid arthritis: use of decision analysis and Markov models.

OBJECTIVE: The management of patients with rheumatoid arthritis (RA) is controversial, with a number of different proposed treatment strategies based on different conceptions of the natural history of the disease and different interpretations of the efficacy and effectiveness of the drugs used for treatment. We attempted to develop a theoretical framework to assess the effectiveness of different treatment regimens for RA. METHODS: We used decision analysis to structure the problem of comparing sequential monotherapy to a combination strategy. Subsequently, we used 3 different estimates of drug effectiveness: one from expert rheumatologists; a metaanalysis; and a recent nationwide survey of American rheumatologists, in a Markov model. Last, we utilized published duration of therapy data to model drug treatment over time. RESULTS: Estimates of drug effectiveness differed substantially among rheumatologists, but regardless of the estimates and the treatment strategy used, the model predicted over 90% of patients improved by the 3rd drug trial. Over time, treatment patterns in our model resemble the "sawtooth" pattern previously observed. CONCLUSION: Treatment strategies in RA are difficult to model because of uncertainty in both the structure of the model and the data needed to perform the analysis. These models tend to overestimate the effectiveness of drug sequences because of nonindependence between therapies, probably due to sequence effects, a change in responsiveness over time, or resistant subgroups. Our preliminary analysis suggests that the most effective agent, possibly methotrexate, should be used first if the objective is to get as many patients into remission as quickly as possible.

Arthritis, Rheumatoid↗

PubMed2006

Testing effective connectivity changes with structural equation modeling: what does a bad model tell us?

Structural equation modeling (SEM) is a statistical method that can assess changes in effective connectivity across tasks or between groups. In its initial application to neuroimaging data, anatomical connectivity provided the constraints to decompose interregional covariances to estimate effective connections. There have been concerns expressed, however, with the validity of interpreting effective connections for a model that does not adequately fit the data. We sought to address this concern by creating two population networks with different patterns of effective connectivity, extracting three samples sizes (N = 100, 60, 20), and then assessing whether the ability to detect effective connectivity differences depended on absolute model fit. Four scenarios were assessed: (1) elimination of a region showing no task differences; (2) elimination of connections with no task differences; (3) elimination of connections that carried task differences, but could be expressed through alternative indirect routes; (4) elimination of connections that carried task differences, and could not be expressed through indirect routes. We were able to detect task differences in all four cases, despite poor absolute model fit. In scenario 3, total effects captured the overall task differences even though the direct effect was no longer present. In scenario 4, task differences that were included in the model remained, but the missing effect was not expressed. In conclusion, it seems that when independent information (e.g., anatomical connectivity) is used to define the causal structure in SEM, inferences about task- or group-dependent changes are valid regardless of absolute model fit.

Algorithms↗

PubMed2005

Multilevel generalized linear models for modelling age-related gender difference in violent behaviour and associated factors in the general household population.

It is preferable to use longitudinal data when studying patterns of violence and antisocial behaviour over the lifespan together with the associated risk factors in the general population. From the statistical modelling perspective, random samples of cross-sectional data, representative of the population, can be a reliable alternative. Sampling, weighting, and possible geographical clustering of the behaviour must be considered in the analysis together with correct choice of model as a function of age, although cohort effects and age effects are not separated from the analysis. This paper demonstrates the use of multilevel generalized linear models in the British National Survey of Psychiatric Morbidity in 2000. A multilevel logistic model as a special case of a generalized linear model with individual weightings was adapted for a dichotomous measure of violence and extended to Poisson and negative binomial outcomes. Three types of age function, discrete age effects, continuous age effects, and piecewise polynomial function of age intervals were evaluated for goodness of fit, and for their practical advantages and disadvantages. Models were developed for possible risk factors in relation to specific age groups of interest.

Adolescent↗

PubMed2004

A sample size computation method for non-linear mixed effects models with applications to pharmacokinetics models.

We propose a simple method to compute sample size for an arbitrary test hypothesis in population pharmacokinetics (PK) studies analysed with non-linear mixed effects models. Sample size procedures exist for linear mixed effects model, and have been recently extended by Rochon using the generalized estimating equation of Liang and Zeger. Thus, full model based inference in sample size computation has been possible. The method we propose extends the approach using a first-order linearization of the non-linear mixed effects model and use of the Wald chi(2) test statistic. The proposed method is general. It allows an arbitrary non-linear model as well as arbitrary distribution of random effects characterizing both inter- and intra-individual variability of the mixed effects model. To illustrate possible uses of the method we present tables of minimum sample sizes, in particular, with an illustration of the effect of sampling design on sample size. We demonstrate how (D-)optimal or frequent sampling requires fewer subjects in comparison to a sparse sampling design. We also present results from Monte Carlo simulations showing that the computed sample size can produce the desired power. The proposed method greatly reduces computing times compared with simulation-based methods of estimating sample sizes for population PK studies.

Black People↗

PubMed2006

Modelling radiation fluxes in simple and complex environments--application of the RayMan model.

The most important meteorological parameter affecting the human energy balance during sunny weather conditions is the mean radiant temperature T(mrt). It considers the uniform temperature of a surrounding surface giving off blackbody radiation, which results in the same energy gain of a human body given the prevailing radiation fluxes. This energy gain usually varies considerably in open space conditions. In this paper, the model 'RayMan', used for the calculation of short- and long-wave radiation fluxes on the human body, is presented. The model, which takes complex urban structures into account, is suitable for several applications in urban areas such as urban planning and street design. The final output of the model is, however, the calculated T(mrt), which is required in the human energy balance model, and thus also for the assessment of the urban bioclimate, with the use of thermal indices such as predicted mean vote (PMV), physiologically equivalent temperature (PET) and standard effective temperature (SET*). The model has been developed based on the German VDI-Guidelines 3789, Part II (environmental meteorology, interactions between atmosphere and surfaces; calculation of short- and long-wave radiation) and VDI-3787 (environmental meteorology, methods for the human-biometeorological evaluation of climate and air quality for urban and regional planning. Part I: climate). The validation of the results of the RayMan model agrees with similar results obtained from experimental studies.

Atmosphere↗

PubMed2006

Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model.

The plasma concentration-time profile of a drug is essential to explain the relationship between the administered dose and the kinetics of drug action. However, in some cases such as in pre-clinical pharmacology or phase-III clinical studies where it is not always possible to collect all the required PK information, this relationship can be difficult to establish. In these circumstances several authors have proposed simple models that can analyse and simulate the kinetics of the drug action in the absence of PK data. The present work further develops and evaluates the performance of such an approach. A virtual compartment representing the biophase in which the concentration is in equilibrium with the observed effect is used to extract the (pharmaco)kinetic component from the pharmacodynamic data alone. Parameters of this model are the elimination rate constant from the virtual compartment (KDE), which describes the equilibrium between the rate of dose administration and the observed effect, and the second parameter, named EDK(50) which is the apparent in vivo potency of the drug at steady state, analogous to the product of EC(50), the pharmacodynamic potency, and clearance, the PK "potency" at steady state. Using population simulation and subsequent (blinded) analysis to evaluate this approach, it is demonstrated that the proposed model usually performs well and can be used for predictive simulations in drug development. However, there are several important limitations to this approach. For example, the investigated doses should extend from those producing responses well below the EC(50) to those producing ones close to the maximum response, optimally reach steady state response and followed until the response returns to baseline. It is shown that large inter-individual variability on PK-PD parameters will produce biases as well as large imprecision on parameter estimates. It is also clear that extrapolations to dosage routes or schedules other than those used to estimate the parameters should be undertaken with great caution (e.g., in case of non-linearity or complex drug distribution). Consequently, it is advised to apply this approach only when the underlying structural PD and PK are well understood. In any case, K-PD model should definitively not be substituted for the gold standard PK-PD model when correct full model can and should be identified.

Adenosine↗

PubMed2005

Bayesian model selection and averaging in additive and proportional hazards models.

Although Cox proportional hazards regression is the default analysis for time to event data, there is typically uncertainty about whether the effects of a predictor are more appropriately characterized by a multiplicative or additive model. To accommodate this uncertainty, we place a model selection prior on the coefficients in an additive-multiplicative hazards model. This prior assigns positive probability, not only to the model that has both additive and multiplicative effects for each predictor, but also to sub-models corresponding to no association, to only additive effects, and to only proportional effects. The additive component of the model is constrained to ensure non-negative hazards, a condition often violated by current methods. After augmenting the data with Poisson latent variables, the prior is conditionally conjugate, and posterior computation can proceed via an efficient Gibbs sampling algorithm. Simulation study results are presented, and the methodology is illustrated using data from the Framingham heart study.

Age of Onset↗

PubMed2006

Simple stochastic fingerprints towards mathematical modeling in biology and medicine 2. Unifying Markov model for drugs side effects.

Most of present mathematical models for biological activity consider just the molecular structure. In the present article we pretend extending the use of Markov chain models to define novel molecular descriptors, which consider in addition other parameters like target site or biological effect. Specifically, this mathematical model takes into consideration not only the molecular structure but the specific biological system the drug affects too. Herein, a general Markov model is developed that describes 19 different drugs side effects grouped in eight affected biological systems for 178 drugs, being 270 cases finally. The data was processed by linear discriminant analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/95.8% for endocrine manifestations, (18 out of 18)/(13 out of 14); 90.5/92.3% for gastrointestinal manifestations, (38 out of 42)/(30 out of 32); 88.5/86.5% for systemic phenomena, (23 out of 26)/(17 out of 20); 81.8/77.3% for neurological manifestations, (27 out of 33)/(19 out of 25); 81.6/86.2% for dermal manifestations, (31 out of 38)/(25 out of 29); 78.4/85.1% for cardiovascular manifestation, (29 out of 37)/(24 out of 28); 77.1/75.7% for breathing manifestations, (27 out of 35)/(20 out of 26) and 75.6/75% for psychiatric manifestations, (31 out of 41)/(23 out of 31). Additionally a back-projection analysis (BPA) was carried out for two ulcerogenic drugs to prove in structural terms the physical interpretation of the models obtained. This article develops a mathematical model that encompasses a large number of drugs side effects grouped in specifics biological systems using stochastic absolute probabilities of interaction ((A)pi(k)(j)) by the first time.

Drug-Related Side Effects and Adverse Reactions↗

PubMed1993

Spreading of excitation in 3-D models of the anisotropic cardiac tissue. I. Validation of the eikonal model.

In this work we investigate, by means of numerical simulations, the performance of two mathematical models describing the spread of excitation in a three dimensional block representing anisotropic cardiac tissue. The first model is characterized by a reaction-diffusion system in the transmembrane and extracellular potentials v and u. The second model is derived from the first by means of a perturbation technique. It is characterized by an eikonal equation, nonlinear and elliptic in the activation time psi(x). The level surfaces psi(x) = t represent the wave-front positions. The numerical procedures based on the two models were applied to test functions and to excitation processes elicited by local stimulations in a relatively small block. The results are in excellent agreement, and for the same problem the computation time required by the eikonal equation is a small fraction of that needed for the reaction-diffusion system. Thus we have strong evidence that the eikonal equation provides a reliable and numerically efficient model of the excitation process. Moreover, numerical simulations have been performed to validate an approximate model for the extracellular potential based on knowledge of the excitation sequence. The features of the extracellular potential distribution affected by the anisotropic conductivity of the medium were investigated.

Animals↗

PubMed2006

C. elegans models of age-associated neurodegenerative diseases: lessons from transgenic worm models of Alzheimer's disease.

Caenorhabditis elegans has been used to model aspects of a number of age-associated neurodegenerative diseases, including Alzheimer's, Parkinson's and Huntington's diseases. These models have typically involved the transgenic expression of disease-associated human proteins. Here I describe my laboratory's specific experience engineering C. elegans models of Alzheimer's disease, and give a general consideration of the advantages and disadvantages of these C. elegans models. The type of insights that might be gained from using these (relatively) simple models are highlighted. In particular, I consider the potential these models have for uncovering common and unique fundamental toxic mechanisms underlying human neurodegenerative diseases.

Aging↗