Search Results (4)

We demonstrate that the potential bias in the scores generated by individual classifiers negatively affects their fusion. Consequently, we present an algorithm to improve the effectiveness of score fusion in classification. The algorithm corrects the score class conditional bias before fusion. The interest of the procedure is demonstrated theoretically, first in general terms and then considering exponential models for the score class conditional distributions. The case of beta distributions is also addressed using Monte Carlo simulations. Finally, a real-life application of fusion of two modalities (EEG, ECG) and two classifiers (Gaussian Bayes and Logistic Regression) is included, showing significant improvement with respect to conventional fusion without bias correction. Full article

Background: Cefiderocol is a siderophore cephalosporin antibiotic active against Gram-negative bacteria, including extended-spectrum beta-lactamase and carbapenemase-producing strains. The pharmacokinetics of cefiderocol has been studied in healthy subjects and particularly in phase II and III studies. This retrospective study investigated intravenous cefiderocol population pharmacokinetics in adult patients treated by cefiderocol. Methods: We studied 55 consecutive patients hospitalized in an intensive care unit. Cefiderocol plasma samples were obtained on different occasions during treatment. Plasma concentration was assayed using mass spectrometry. Data analysis was performed using a non-linear mixed-effect approach via Monolix 2020R1. Results: A total of 205 plasma samples were obtained from 55 patients. Eighty percent of patients received cefiderocol for ventilator-associated pneumonia due to carbapenem-resistant Pseudomonas aeruginosa infection. Cefiderocol concentration time-courses were best fit to a two-compartment open model with first-order elimination. Elimination clearance was positively related to renal function (estimated by the CKD formula). Adding albumin plasma binding in the model significantly improved the model assuming a ~40% unbound drug fraction given a ~40 g/L albuminemia. The final model included CKD plus cefiderocol plasma binding effects. Fat-free mass was better than total body weight to influence, via the allometric rule, clearance and volume terms, but this effect was negligible. The final clearance based on free circulating drug (CL_U) for a typical patient, CKD = 90, was 7.38 L/h [relative standard error, RSE, 22%] with a between-subject variability of 0.47 [RSE 10%] (exponential distribution). Conclusion: This study showed that albumin binding and CKD effects were significant predictors of unbound and total plasma cefiderocol concentrations. Our results indicate that individual adjustment of cefiderocol can be used to reach high minimum inhibitory concentrations based on an estimation of unbound drug concentration and optimize therapeutic efficacy. Full article

When modeling real phenomena, special cases of the generalized gamma distribution and the generalized beta distribution of the second kind play an important role. The paper discusses the gamma-exponential distribution, which is closely related to the listed ones. The asymptotic normality of the previously obtained strongly consistent estimators for the bent, shape, and scale parameters of the gamma-exponential distribution at fixed concentration parameters is proved. Based on these results, asymptotic confidence intervals for the estimated parameters are constructed. The statements are based on the method of logarithmic cumulants obtained using the Mellin transform of the considered distribution. An algorithm for filtering out unnecessary solutions of the system of equations for logarithmic cumulants and a number of examples illustrating the results obtained using simulated samples are presented. The difficulties arising from the theoretical study of the estimates of concentration parameters associated with the inversion of polygamma functions are also discussed. The results of the paper can be used in the study of probabilistic models based on continuous distributions with unbounded non-negative support. Full article

Inverse Rayleigh probability distribution is shown in this paper to constitute a valid model for characterization and estimation of extreme values of wind speed, thus constituting a useful tool of wind power production evaluation and mechanical safety of installations. The first part of this paper illustrates such a model and its validity to interpret real wind speed field data. The inverse Rayleigh model is then adopted as the parent distribution for assessment of a dynamical “risk index” defined in terms of a stochastic Poisson process, based upon crossing a given value with part of the maximum value of wind speed on a certain time horizon. Then, a novel Bayes approach for the estimation of such an index under the above model is proposed. The method is based upon assessment of prior information in a novel way which should be easily feasible for a system engineer, being based upon a model quantile (e.g., the median value) or, alternatively, on the probability that the wind speed is greater than a given value. The results of a large set of numerical simulation—based upon typical values of wind-speed parameters—are reported to illustrate the efficiency and the precision of the proposed method, also with hints to its robustness. The validity of the approach is also verified with respect to the two different methods of assessing the prior information. Full article