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PubMed1998

A risk model approach to the prediction of fetal acidemia.

Known risk factors for adverse perinatal outcome were related to severe fetal acidemia during labor and at birth. Severe fetal acidemia was defined as a fetal scalp or umbilical artery pH > 3 SD below the mean for normal pregnancy. pH values were available for 1,524 singleton births without congenital anomalies. Of these births, 83 (5.4%) had at least one pH value > 3 SD below the reference mean. Known risk factors in the group with severe acidemia were compared with those in a random sample of the remaining 94.6% births. The strongest predictor of severe fetal acidemia was an abnormal cardiotocogram with a Hammacher score > 8. Risk factors shown to lower pH values significantly were combined in a prediction model based on multiple regression analysis. Back-validation of the model showed that half of all cases of severe fetal acidemia could not be adequately predicted. It is concluded that such prediction models do not contribute to a more effective detection of severe fetal acidemia.

Acidosis↗

PubMed2005

Quantifying the robustness of a broth-based model for predicting Listeria monocytogenes growth in meat and poultry products.

Given the importance of Listeria monocytogenes as a risk factor in meat and poultry products, there is a need to evaluate the relative robustness of predictive growth models applied to meat products. The U.S. Department of Agriculture-Agricultural Research Service Pathogen Modeling Program is a tool widely used by the food industry to estimate pathogen growth, survival, and inactivation in food. However, the robustness of the Pathogen Modeling Program broth-based L. monocytogenes growth model in meat and poultry application has not, to our knowledge, been specifically evaluated. In the present study, this model was evaluated against independent data in terms of predicted microbial counts and covered a range of conditions inside and outside the original model domain. The robustness index was calculated as the ratio of the standard error of prediction (root mean square error of the model against an independent data set not used to create the model) to the standard error of calibration (root mean square error of the model against the data set used to create the model). Inside the calibration domain of the Pathogen Modeling Program, the best robustness index for application to meat products was 0.37; the worst was 3.96. Outside the domain, the best robustness index was 0.40, and the worst was 1.22. Product type influenced the robustness index values (P < 0.01). In general, the results indicated that broth-based predictive models should be validated against independent data in the domain of interest; otherwise, significant predictive errors can occur.

Animals↗

PubMed1997

Multifactorial modelling for caries prediction in Jordanian university students.

OBJECTIVE: To construct a prediction model for caries experience in Jordanian university students using a number of explanatory risk factors as predictors. DESIGN: Data on salivary flow rate, buffering capacity, streptococci and lactobacilli counts, plaque accumulation, oral hygiene and between meal sugar intakes were tested as predictors of clinically and radiographically registered DMFS: Methods of analysis included correlation, then multiple regression, and finally dichotomisation of the DMFS data and application of discriminant analysis and logistic regression. The latter analyses were conducted in order to predict in which caries risk group an individual belonged rather than predicting (as with regression) their actual caries status. Two dichotomisation schemes were investigated; dichotomisation at the mean and at the 75th percentile. SETTING: The University of Jordan. PARTICIPANTS: A random sample of 180 university students (77 male and 103 female). OUTCOME MEASURES: Relationships were expressed as correlation coefficients, R2, and sensitivity, specificity and predictive values of the predictors, and also their validity and efficiency. RESULTS: The highest correlation coefficient achieved was 0.43 (P < 0.0001) between sugar-containing snack intakes and DMFS. The predicted power of the fitted multiple regression model was low R2 = 0.38). Logistic regression with the DMFS data dichotomised at the 75th percentile indicated that the fitted caries model correctly identified 76 per cent of the subjects. Sensitivity and specificity values of the predictive battery were 80 per cent and 75 per cent respectively. CONCLUSIONS: The multifactorial aetiology of caries remains unclear and requires further research. In the meantime, well-documented preventive measures should be implemented for this and similar populations.

Buffers↗

PubMed2001

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

Infrared Rays↗

PubMed1994

Artificial intelligence versus logistic regression statistical modelling to predict cardiac complications after noncardiac surgery.

The traditional approach to developing models predictive of cardiac events has been to perform logistic regression (LR) analysis on a variety of potential predictors. An alternative to use an artificial intelligence system called a neural network (NN) which simulates biological intelligence. To evaluate the potential applicability of the latter method, we compared the ability of LR and NN techniques to predict cardiac events after noncardiac surgery. A total of 200 patients (training group) underwent cardiac risk assessment before major noncardiac surgery using 17 clinical parameters and 7 quantitative indices based on dipyridamole-thallium imaging. There were 21 post-operative myocardial infarctions and/or cardiac deaths. Data from the training group were used to develop two predictive models: one based on backward stepwise LR multivariate statistical analysis and the other one using a neural network. Both models were then validated on a second group of 160 consecutive patients also referred for preoperative risk stratification (validation group). The NN consisted of 14 input, 29 hidden, and 1 output neurons and used a back-propagation algorithm (learning rate 0.2, training tolerance 0.5, sigmoid transfer function). The sensitivity, specificity, positive and negative predictive accuracies for the prediction of postoperative events in the validation group of 160 patients were, respectively, 67% (6/9), 82% (124/151), 18% (6/33), and 98% (124/127) for LR, and 67% (6/9), 96% (145/151), 50% (6/12), and 98% (145/148) for the NN, with a difference in specificity which attained statistical significance (p < 0.01). Artificial intelligence may provide a useful alternative to conventional LR statistical analysis for the purpose of preoperative cardiac risk assessment.

Artificial Intelligence↗

PubMed2005

Schema-based learning of adaptable and flexible prey- catching in anurans II. Learning after lesioning.

The previous companion paper describes the initial (seed) schema architecture that gives rise to the observed prey-catching behavior. In this second paper in the series we describe the fundamental adaptive processes required during learning after lesioning. Following bilateral transections of the hypoglossal nerve, anurans lunge toward mealworms with no accompanying tongue or jaw movement. Nevertheless anurans with permanent hypoglossal transections eventually learn to catch their prey by first learning to open their mouth again and then lunging their body further and increasing their head angle. In this paper we present a new learning framework, called schema-based learning (SBL). SBL emphasizes the importance of the current existent structure (schemas), that defines a functioning system, for the incremental and autonomous construction of ever more complex structure to achieve ever more complex levels of functioning. We may rephrase this statement into the language of Schema Theory (Arbib 1992, for a comprehensive review) as the learning of new schemas based on the stock of current schemas. SBL emphasizes a fundamental principle of organization called coherence maximization, that deals with the maximization of congruence between the results of an interaction (external or internal) and the expectations generated for that interaction. A central hypothesis consists of the existence of a hierarchy of predictive internal models (predictive schemas) all over the control center-brain-of the agent. Hence, we will include predictive models in the perceptual, sensorimotor, and motor components of the autonomous agent architecture. We will then show that predictive models are fundamental for structural learning. In particular we will show how a system can learn a new structural component (augment the overall network topology) after being lesioned in order to recover (or even improve) its original functionality. Learning after lesioning is a special case of structural learning but clearly shows that solutions cannot be known/hardwired a priori since it cannot be known, in advance, which substructure is going to break down.

Animals↗

PubMed2004

Calculation of the peak concentration of 2-chlorobenzamide generated in the hydrolysis of the benzoylphenylurea insecticide 1-(2-chlorobenzoyl)-3-(4-chlorophenyl)urea.

1-(2-chlorobenzoyl)-3-(4-chlorophenyl)urea (CCU), a new analogue of diflubenuron and PH-6038, has been widely used in agriculture and forestry as a molt-inhibiting hormone insecticide which was developed in China. 2-Chlorobenzamide, a main degradation product of CCU in the environment, has been identified as a potential carcinogen, so the content of 2-chlorobenzamide from the breakup of CCU will directly affect the environmental safety of CCU. In this paper we describe a simple, rapid, and convenient prediction model for predicting the level and time of occurrence of the peak concentration of 2-chlorobenzamide in the hydrolysis of CCU verified by experimental data. The time for reaching the peak concentration of 2-chlorobenzamide (tm) at 25 degrees C and pH 6 is 13.5 d, and the maximum concentration of 2-chlorobenzamide (ym) is 3.2% of the initial concentration of CCU according to the results from the prediction model. These results are similar to the real values from the experiments, which are 22 d and 1.6% of the initial concentration of CCU, respectively. The difference between the values of the prediction and experiment is discussed, and it is demonstrated that the predicting model is highly credible.

Benzamides↗

PubMed2003

Development and field validation of a predictive copper toxicity model for the green alga Pseudokirchneriella subcapitata.

In this study, the combined effects of pH, water hardness, and dissolved organic carbon (DOC) concentration and type on the chronic (72-h) effect of copper on growth inhibition of the green alga Pseudokirchneriella subcapitata were investigated. Natural dissolved organic matter (DOM) was collected at three sites in Belgium and The Netherlands using reverse osmosis. A full central composite test design was used for one DOM and a subset of the full design for the two other DOMs. For a total number of 35 toxicity tests performed, 72-h effect concentration resulting in 10% growth inhibition (EbC10s) ranged from 14.2 to 175.9 micrograms Cu/L (factor 12) and 72-h EbC50s from 26.9 to 506.8 micrograms Cu/L (factor 20). Statistical analysis demonstrated that DOC concentration, DOM type, and pH had a significant effect on copper toxicity; hardness did not affect toxicity at the levels tested. In general, an increase in pH resulted in increased toxicity, whereas an increase of the DOC concentration resulted in decreased copper toxicity. When expressed as dissolved copper, significant differences of toxicity reduction capacity were noted across the three DOM types tested (up to factor 2.5). When expressed as Cu2+ activity, effect levels were only significantly affected by pH; linear relationships were observed between pH and the logarithm of the effect concentrations expressed as free copper ion activity, that is, log(EbC50Cu2+) and log(EbC10Cu2+): (1) log(EbC50Cu2+)= - 1.431 pH + 2.050 (r2 = 0.95), and (2) log(EbC10cu2+) = -1.140 pH -0.812 (r2 = 0.91). A copper toxicity model was developed by linking these equations to the WHAM V geochemical speciation model. This model predicted 97% of the EbC50dissolved and EbC10dissolved values within a factor of two of the observed values. Further validation using toxicity test results that were obtained previously with copper-spiked European surface waters demonstrated that for 81% of tested waters, effect concentrations were predicted within a factor of two of the observed. The developed model is considered to be an important step forward in accounting for copper bioavailability in natural systems.

Biological Availability↗

PubMed2005

The epidemiological impact of antiretroviral use predicted by mathematical models: a review.

This review summarises theoretical studies attempting to assess the population impact of antiretroviral therapy (ART) use on mortality and HIV incidence. We describe the key parameters that determine the impact of therapy, and argue that mathematical models of disease transmission are the natural framework within which to explore the interaction between antiviral use and the dynamics of an HIV epidemic. Our review focuses on the potential effects of ART in resource-poor settings. We discuss choice of model type and structure, the potential for risk behaviour change following widespread introduction of ART, the importance of the stage of HIV infection at which treatment is initiated, and the potential for spread of drug resistance. These issues are illustrated with results from models of HIV transmission. We demonstrate that HIV transmission models predicting the impact of ART use should incorporate a realistic progression through stages of HIV infection in order to capture the effect of the timing of treatment initiation on disease spread. The realism of existing models falls short of properly reproducing patterns of diagnosis timing, incorporating heterogeneity in sexual behaviour, and describing the evolution and transmission of drug resistance. The uncertainty surrounding certain effects of ART, such as changes in sexual behaviour and transmission of ART-resistant HIV strains, demands exploration of best and worst case scenarios in modelling, but this must be complemented by surveillance and behavioural surveys to quantify such effects in settings where ART is implemented.

Journal Article↗

PubMed1996

Orthogonal motion after-effect illusion predicted by a model of cortical motion processing.

The motion after-effect occurs after prolonged viewing of motion; a subsequent stationary scene is perceived as moving in the opposite direction. This illusion is thought to arise because motion is represented by the differential activities of populations of cortical neurons tuned to opposite directions; fatigue in one population leads to an imbalance that favours the opposite direction once the stimulus ceases. Following adaptation to multiple directions of motion, the after-effect is unidirectional, indicating that motion signals are integrated across all directions. Yet humans can perceive several directions of motion simultaneously. The question therefore arises as to how the visual system can perform both sharp segregation and global integration of motion signals. Here we show in computer simulations that this can occur if excitatory interactions between different directions are sharply tuned while inhibitory interactions are broadly tuned. Our model predicts that adaptation to simultaneous motion in opposite directions will lead to an orthogonal motion after-effect. This prediction was confirmed in psychophysical experiments. Thus, broadly tuned inhibitory interactions are likely to be important in the integration and segregation of motion signals. These interactions may occur in the cortical area MT, which contains motion-sensitive neurons with properties similar to those required by our model.

Afterimage↗

PubMed2002

Computational models for cytochrome P450: a predictive electronic model for aromatic oxidation and hydrogen atom abstraction.

Experimental observations suggest that electronic characteristics play a role in the rates of substrate oxidation for cytochrome P450 enzymes. For example, the tendency for oxidation of a certain functional group generally follows the relative stability of the radicals that are formed (e.g., N-dealkylation > O-dealkylation > 2 degrees carbon oxidation > 1 degree carbon oxidation). In addition, results show that useful correlations between the rates of product formation can be developed using electronic models. In this article, we attempt to determine whether a combined computational model for aromatic and aliphatic hydroxylation can be developed. Toward this goal, we used a combination of experimental data and semiempirical molecular orbital calculations to predicted activation energies for aromatic and aliphatic hydroxylation. The resulting model extends the predictive capacity of our previous aliphatic hydroxylation model to include the second most important group of oxidations, aromatic hydroxylation. The combined model can account for about 83% of the variance in the data for the 20 compounds in the training set and has an error of about 0.7 kcal/mol.

Computer Simulation↗

PubMed1996

A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area.

Prediction of ambient ozone concentrations in urban areas would allow evaluation of such factors as compliance and noncompliance with EPA requirements. Though ozone prediction models exist, there is still a need for more accurate models. Development of these models is difficult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, we developed a neural network model for forecasting daily maximum ozone levels. We then compared the neural network's performance with those of two traditional statistical models, regression, and Box-Jenkins ARIMA. The neural network model for forecasting daily maximum ozone levels is different from the two statistical models because it employs a pattern recognition approach. Such an approach does not require specification of the structural form of the model. The results show that the neural network model is superior to the regression and Box-Jenkins ARIMA models we tested.

Journal Article↗

PubMed2004

Preoperative risk factors for common bile duct stones: defining the patient at high risk in the laparoscopic cholecystectomy era.

BACKGROUND: Common bile duct stones (CBDS) are present in 3-20% of patients with cholelithiasis. Intraoperative cholangiography has high sensitivity in detecting CBDS but its routine use is associated with increased costs and operating room time. The aim of our study was to define an accurate and simple model for the prediction of CBDS using preoperative variables. METHODS: The study consisted of a retrospective analysis followed by a prospective study. Multivariate analysis of the retrospective data was used to create a predictive model for the presence of concurrent CBDS in patients undergoing cholecystectomy. The predictive model was then validated in a prospective series of 160 patients undergoing laparoscopic cholecystectomy. RESULTS: Among the 19 potentially predictive variables for CBDS, only four were found to be statistically significant and independent: X1-alkaline phosphatase levels (UI/L); X2-number of gallbladder stones; X3-total serum bilirubin (mg/dL); and X4-CBD diameter (mm). Using these four variables, the multivariate analysis created the equation: score = 0.002 x X1 + 0.485 x X2 + 0.232 x X3 + 0.220 x X4 - 4.167 to define the risk of CBDS in each patient. The predictive model, tested prospectively in 160 patients undergoing laparoscopic cholecystectomy (LC), showed an elevated index of correlation (r = 0.75) among the predicted and the observed frequencies (chi2 = 126.6; P < 0.0001). The predictive model sensitivity and specificity were 92.9% and 99.3%, respectively. CONCLUSIONS: In patients undergoing cholecystectomy, accurate prediction of the risk for concurrent CBDS can be achieved using four preoperative variables. The use of this predictive model can contribute to reducing the number of unnecessary common bile duct explorations.

Adult↗

PubMedDate not supplied

Search for predictive generic model of aqueous solubility using Bayesian neural nets.

Several predictive models of aqueous solubility have been published. They have good performances on the data sets which have been used for training the models, but usually these data sets do not contain many structures similar to the structures of interest to the drug research and their applicability in drug hunting is questionable. A very diverse data set has been gathered with compounds issued from literature reports and proprietary compounds. These compounds have been grouped in a so-called literature data set I, a proprietary data set II, and a mixed data set III formed by I and II. About 100 descriptors emphasizing surface properties were calculated for every compound. Bayesian learning of neural nets which cumulates the advantages of neural nets without having their weaknesses was used to select the most parsimonious models and train them, from I, II, and III. The models were established by either selecting the most efficient descriptors one by one using a modified Gram-Schmidt procedure (GS) or by simplifying a most complete model using automatic relevance procedure (ARD). The predictive ability of the models was accessed using validation data sets as much unrelated to the training sets as possible, using two new parameters: NDD(x,ref) the normalized smallest descriptor distance of a compound x to a reference data set and CD(x,mod) the combination of NDD(x,ref) with the dispersion of the Bayesian neural nets calculations. The results show that it is possible to obtain a generic predictive model from database I but that the diversity of database II is too restricted to give a model with good generalization ability and that the ARD method applied to the mixed database III gives the best predictive model.

Journal Article↗

PubMed2006

Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays.

BACKGROUND: Genetic markers hold great promise for refining our ability to establish precise prognostic prediction for diseases. The development of comprehensive gene expression microarray technology has allowed the selection of relevant marker genes from a large pool of candidate genes in early-phased, developmental prognostic marker studies. The primary analytical task in such studies is to select a small fraction of relevant genes, typically from a list of significant genes, for further investigation in subsequent studies. RESULTS: We develop a methodology for predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays. Key components in this methodology include building prediction models, assessing predictive performance of prediction models, and assessing significance of prediction results. As particular specifications, we assume Cox proportional hazard models with a compound covariate. For assessing predictive accuracy, we propose to use the cross-validated log partial likelihood. To assess significance of prediction results, we apply permutation procedures in cross-validated prediction. As an additional key component peculiar to prognostic prediction, we also consider incorporation of standard prognostic factors. The methodology is evaluated using both simulated and real data. CONCLUSION: The developed methodology for prognostic prediction using a subset of significant genes can provide new insights based on predictive capability, possibly incorporating standard prognostic factors, in selecting a fraction of relevant genes for subsequent studies.

Biomarkers↗

PubMed2003

Predictors of successful arteriovenous fistula maturation.

BACKGROUND: Understanding the factors associated with successful arteriovenous fistula (AVF) maturation may provide an opportunity to improve vascular access outcomes. METHODS: This prospective cohort study enrolled 348 hemodialysis patients receiving an AVF and followed them until ascertainment of successful AVF maturation. Multivariable logistic regression was used to develop (1) explanatory models of factors associated with AVF success and (2) models for predicting success based on sociodemographic and comorbidity profiles. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative ability of our predictive model. Predictive probabilities of AVF maturation under the assumption of optimal surgical technique defined by explanatory models were calculated. RESULTS: A total of 55.5% of AVFs successfully matured. A history of stroke or transient ischemic attack, increasing age, and dependence on dialysis at the time of access placement were associated with lower probabilities of maturation. Of potentially modifiable variables, maturation was associated with greater intraoperative doses of heparin, use of large-diameter veins, and mean arterial pressure of 85 mm Hg or greater. Using optimal surgical technique, the probability of successful AVF maturation would have been as high as 84%. A predictive logistic regression model had moderate ability to predict AVF maturation under optimal surgical therapy with an area under the ROC curve of 0.69. CONCLUSION: Surgical technique potentially may be modified to yield a greater probability of successful AVF maturation. Predictive probabilities of successful AVF maturation under optimal surgical technique reinforce the view that functioning AVFs can be achieved in a large proportion of hemodialysis patients, consistent with experience in selected regions of the United States and abroad.

Adult↗

PubMed2005

Applying a neural network to predict the thermodynamic parameters for an expanded nearest-neighbor model.

Predicting the secondary and tertiary structure of RNAs largely depends on our capabilities in estimating the thermodynamics of RNA duplexes. In this work, an expanded nearest-neighbor model, designated INN-48, is established. The thermodynamic parameters of this model are predicted using both multiple linear regression analysis and neural network analysis. It is suggested that due to the increase in the number of parameters and the insufficiency of the existing data, neural network analysis results in more reliable predictions. Furthermore, it is suggested that INN-48 can be used to estimate the thermodynamics of RNA duplex formation for longer sequences, whereas INN-HB, the previous model on which INN-48 is based, can be used for short sequences.

Animals↗

PubMed2000

Predictive exposure modelling--a case study with a detergent surfactant.

Environmental exposure estimations are generally based on a knowledge of how and in what quantity a substance enters the environment and how it may subsequently be distributed and transformed. Once present within the environment, biota (including man) may be exposed. This paper outlines the tools commonly used to estimate environmental exposure to ingredients from detergents and other household products. Such products are typically manufactured in large quantities, used by many people, and disposed of after household use into the environment via the sewer. The vast majority of this waste stream is treated via domestic wastewater treatment plants (WWTPs) as documented in the sewage treatment Directive 91/275/EEC. WWTPs significantly reduce the load of chemical substances to the receiving surface waters, and have become an intrinsic part of exposure and risk assessment of household chemicals. WWTP models are generally first-order (e.g. SIMPLETREAT, WWTREAT) or mixed-order (e.g. Monod) kinetics and can exhibit, potentially, very distinct dependencies on the influent concentration. Thus, the correct representation of xenobiotic behaviour in a WWTP and modeling of their fate has a significant impact on exposure assessment. The Environmental Risk Assessment Steering Committee (ERASM) of the Association Internationale de la Savonnerie et la Détergence, et des Produits d'Entretiens (AISE) and the Comité Européen de Agents de Surface et Intermédiares Organiques (CESIO) has commissioned a joint industry Task Force of the Association to develop and apply specific methodology for the environmental monitoring of surfactants, and verification of fate models. The monitoring programmes have been designed to (1) establish the fate, distribution and concentrations of the major surfactants used in detergents-linear alkylbenzene sulfonate (LAS), alcohol ethoxylates (AE), alcohol ethoxylated sulfates (AES) and soap in relevant environmental compartments and (2) to provide the necessary data for checking the applicability of mathematical models to predict their fate and concentrations in these environmental compartments. The case study detailed here, specifically focuses on the refinement of the LAS exposure assessment for surface waters in The Netherlands.

Animals↗