Optimal Probability Research Articles

Comparing the Hospital Frailty Risk Score and the Clinical Frailty Scale Among Older Adults With Chronic Obstructive Pulmonary Disease Exacerbation

Frailty is associated with severe morbidity and mortality among people with chronic obstructive pulmonary disease (COPD). Interventions such as pulmonary rehabilitation can treat and reverse frailty, yet frailty is not routinely measured in pulmonary clinical practice. It is unclear how population-based administrative data tools to screen for frailty compare with standard bedside assessments in this population. To determine the agreement between the Hospital Frailty Risk Score (HFRS) and the Clinical Frailty Scale (CFS) among hospitalized individuals with COPD and to determine the sensitivity and specificity of the HFRS (vs CFS) to detect frailty. A cross-sectional study was conducted among hospitalized patients with COPD exacerbation. The study was conducted in the respiratory ward of a single tertiary care academic hospital (The Ottawa Hospital, Ottawa, Ontario, Canada). Participants included consenting adult inpatients who were admitted with a diagnosis of acute COPD exacerbation from December 2016 to June 2019 and who used a clinical care pathway for COPD. There were no specific exclusion criteria. Data analysis was performed in March 2022. Degree of frailty measured by the CFS. The HFRS was calculated using hospital administrative data. Primary outcomes were the sensitivity and specificity of the HFRS to detect frail and nonfrail individuals according to CFS assessments of frailty, and the secondary outcome was the optimal probability threshold of the HFRS to discriminate frail and nonfrail individuals. Among 99 patients with COPD exacerbation (mean [SD] age, 70.6 [9.5] years; 56 women [57%]), 14 (14%) were not frail, 33 (33%) were vulnerable, 18 (18%) were mildly frail, and 34 (34%) were moderately to severely frail by the CFS. The HFRS (vs CFS) had a sensitivity of 27% and specificity of 93% to detect frail vs nonfrail individuals. The optimal probability threshold for the HFRS was 1.4 points or higher. The corresponding sensitivity to detect frailty was 69%, and the specificity was 57%. In this cross-sectional study, using the population-based HFRS to screen for frailty yielded poor detection of frailty among hospitalized patients with COPD compared with the bedside CFS. These findings suggest that use of the HFRS in this population may result in important missed opportunities to identify and provide early intervention for frailty, such as pulmonary rehabilitation.

IATLR: Improved ACO and TOPSIS-Based Layering Routing Protocol for Underwater Acoustic Networks

Aiming at the challenges of underwater acoustic networks (UANs) such as low bandwidth, long propagation delay, dynamic network topology, and energy limitation, an improved ant colony optimization (IACO) and technique for order preference by similarity to an ideal solution (TOPSIS)-based layering routing (IATLR) protocol for UANs is proposed. In the IATLR protocol, the transfer probability of the ant colony optimization (ACO) is optimized by comprehensively considering multiple factors such as energy consumption, propagation distance, and forwarding angle. Moreover, the ACO is improved and the optimized ACO is combined with the TOPSIS method by using the pheromone concentration of ACO as the decision basis of TOPSIS. Furthermore, the multifactor decision ranking of the TOPSIS method is optimized by the transfer probability of the ACO. Consequently, the best next-hop node is determined. In addition, the energy consumption is balanced through the whole network by the node silence mechanism in the IATLR protocol. Simulation results show that the IATLR protocol has significant advantages compared with the depth-based routing for underwater sensor networks (DBR), layer-based energy-efficient routing (LEER), and depth and distance based layering routing protocol for underwater sensor networks (DDLR) routing protocols in terms of packet delivery rate (PDR), energy consumption, and end-to-end delay (EED). The PDR increases by 5%–30%, the EED decreases by about 1–7 s, and the average energy consumption (AEC) reduces by 0.5–6 J.

Acid-base imbalances and the association of blood-gas variables, electrolytes, and biochemical analytes with outcome in hospitalized calves undergoing abdominal surgery.

Surgical abdominal emergencies in calves are associated with a guarded prognosis and have the potential for complex metabolic derangements including acid-base imbalances. To perform a comprehensive analysis of acid-base status and to assess the prognostic relevance of preoperative clinicopathologic variables in calves undergoing abdominal surgery. Hospital-based study samples of 535 (dataset 1; DS1) and 83 calves (dataset 2; DS2). Retrospective (DS1) and prospective (DS2) case series. In DS1, acidemia (pH <7.33) was present in 49.9%, whereas alkalemia (pH >7.37) was present in 30.7% of calves. Plasma L-lactate, chloride, and serum inorganic phosphorus concentration accounted for 51.9%, 11.6% and 9.4% of the variation of venous blood pH, respectively. Classification tree analysis indicated that a negative outcome (death or euthanasia during hospitalization) was associated with venous pO2 ≤33.6 mm Hg, anion gap >18.3 and >22.9mEq/L, serum albumin concentration ≤36.5 and ≤29.4g/L, serum urea concentration >4.4 mmol/L, and plasma ionized calcium concentration ≤1.26 mmol/L. The area under the receiver operating characteristic curve of this model was 0.85 (95% CI: 0.82-0.89, P < .001) and the resulting sensitivity and specificity for the prediction of nonsurvival at the optimal probability cut-point of 0.5 was 89.8% and 65.7%, respectively. In DS2 the model had a similar sensitivity and specificity of 90.5% and 70%, respectively. Clinicopathologic imbalances and associated changes of acid-base status are common in calves with surgical abdominal emergencies and have clinical utility for the prediction of a negative postoperative outcome.

Optimal sampling algorithms for block matrix multiplication

In this paper, we investigate the randomized algorithms for block matrix multiplication from random sampling perspective. Specifically, based on the A-optimal design criterion, we obtain the optimal sampling probabilities and sampling block sizes. To improve the practicability of the block sizes, two modified ones with less computation cost are provided. With respect to the second modified block size, we devise a two step algorithm. Moreover, the probability error bounds for the proposed algorithms are also given. Extensive numerical results show that our methods outperform the state-of-the-art ones given in the literature.

Prostate cancer lesion detection, volume quantification and high-grade cancer differentiation using cancer risk maps derived from multiparametric MRI with histopathology as the reference standard

Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and location of both all PCa and high grade (Gleason grade group ≥ 3) PCa. Such capabilities would aid physicians and patients in treatment decisions, targeting biopsy, and planning focal therapy. A cohort of men with biopsy proven prostate cancer and pre-prostatectomy mpMRI were studied. PCa and benign ROIs (1524) were identified on mpMRI and histopathology with histopathology serving as the reference standard. Logistic regression models were created to differentiate PCa from benign tissues. The MRI images were registered to ensure correct overlay. The cancer models were applied to each image voxel within prostates to create probability maps of cancer and of high-grade cancer. Use of an optimum probability threshold quantified PCa volume for all lesions >0.1 cc. Accuracies were calculated using area under the curve (AUC) for the receiver operating characteristic (ROC). The PCa models utilized apparent diffusion coefficient (ADC), T2 weighted (T2W), dynamic contrast-enhanced MRI (DCE MRI) enhancement slope, and DCE MRI washout as the statistically significant MRI scans. Application of the PCa maps method provided total PCa volume and individual lesion volumes. The AUCs derived from lesion analysis were 0.91 for all PCa and 0.73 for high-grade PCa. At the optimum threshold, the PCa maps detected 135 / 150 (90%) histopathological lesions >0.1 cc. This study showed the feasibility of cancer risk maps, created from pre-prostatectomy, mpMR images validated with histopathology, to detect PCa lesions >0.1 cc. The method quantified the volume of cancer within the prostate. Method improvements were identified by determining root causes for over and underestimation of cancer volumes. The maps have the potential for improved non-invasive capability in quantitative detection, localization, volume estimation, and MRI characterization of PCa.

Robust certification of unsharp instruments through sequential quantum advantages in a prepare-measure communication game

Communication games are one of the widely used tools that are designed to demonstrate quantum supremacy over classical resources in which two or more parties collaborate to perform an information processing task to achieve the highest success probability of winning the game. We propose a specific two-party communication game in the prepare-measure scenario that relies on an encoding-decoding task of specific information. We first demonstrate that quantum theory outperforms the classical preparation noncontextual theory and the optimal quantum success probability of such a communication game enables the semi-device-independent certification of qubit states and measurements. Further, we consider the sequential sharing of quantum preparation contextuality and show that, at most, two sequential observers can share the quantum advantage. The suboptimal quantum advantages for two sequential observers form an optimal pair which certifies a unique value of the unsharpness parameter of the first observer. Since the practical implementation inevitably introduces noise, we devised a scheme to demonstrate the robust certification of the states and unsharp measurement instruments of both the sequential observers.

Enhanced Robust Univariate Classification Methods for Solving Outliers and Overfitting Problems

The robustness of some classical univariate classifiers is hampered if the data are contaminated. Overfitting is another hiccup when the data sets are uncontaminated with a considerable sample size. The performance of the classification models can be easily biased by the outliers’ problems, of which the constructed model tends to be overfitted. Previous studies often used the Bayes Classifier (BC) and the Predictive Classifier (PC) to address two groups of univariate classification problems. Unfortunately for substantial large sample sizes and uncontaminated data, the BC method overfits when the Optimal Probability of Exact Classification (OPEC) is used as an evaluation benchmark. Meanwhile, for small sample sizes, the BC and PC methods are extremely susceptible to outliers. To overcome these two problems, we proposed two methods: the Smart Univariate Classifier (SUC) and the hybrid classifier. The latter is a combination of the SUC and the BC methods, known as the Smart Univariate Bayes Classifier (SUBC). The performance of the new classification methods was evaluated and compared with the conventional BC and PC methods using the OPEC as a benchmark value. To validate the performance of these classification methods, the Probability of Exact Classification (PEC) was compared with the OPEC value. The results showed that the proposed methods outperformed the conventional BC and PC methods based on the real data sets applied. Numerical results also revealed that the SUC method could solve the overfitting problem. The results further indicated that the two proposed methods were robust against outliers. Therefore, these new methods are helpful when practitioners are confronted with overfitting and data contamination problems.

Multivariable statistical models to predict red cell transfusion in elective surgery.

Predicting red cell transfusion may assist in identifying those most likely to benefit from patient blood management strategies. Our objective was to identify a simple statistical model to predict transfusion in elective surgery from routinely available data. Our final multicentre cohort consisted of 42,546 patients and contained the following potential predictors of red cell transfusion known prior to admission: patient age, sex, pre-admission hemoglobin, surgical procedure, and comorbidities. Missing data were handled by multiple imputation methods. The outcome measure of interest was administration of a red cell transfusion. We used multivariable logistic regression models to predict transfusion, and evaluated the performance by applying a 10-fold cross-validation. Model accuracy was assessed by comparing the area under the receiver operating characteristics curve. After applying an optimal probability cut-off we measured model accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. 7.0% (n=2,993) of the study population received a red cell transfusion. Our most simple model predicted red cell transfusion based on admission hemoglobin and surgical procedure with a multiply imputed estimated area under the curve of 0.862 (0.856, 0.864). The estimated accuracy, sensitivity, specificity, positive predictive, and negative predictive values at the probability cut-off of 0.4 were 0.934, 0.257, 0.986, 0.573, and 0.946 respectively. A small number of variables available prior to admission can predict red cell transfusion with very good accuracy. Our model can be used to flag high-risk patients most likely to benefit from pre-operative patient blood management measures.

The optimal probability of the risk for finite horizon partially observable Markov decision processes

&lt;abstract&gt;&lt;p&gt;This paper investigates the optimality of the risk probability for finite horizon partially observable discrete-time Markov decision processes (POMDPs). The probability of the risk is optimized based on the criterion of total rewards not exceeding the preset goal value, which is different from the optimal problem of expected rewards. Based on the Bayes operator and the filter equations, the optimization problem of risk probability can be equivalently reformulated as filtered Markov decision processes. As an advantage of developing the value iteration technique, the optimality equation satisfied by the value function is established and the existence of the risk probability optimal policy is proven. Finally, an example is given to illustrate the effectiveness of using the value iteration algorithm to compute the value function and optimal policy.&lt;/p&gt;&lt;/abstract&gt;

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

When training for hazardous operations, real-time stress detection is an asset for optimizing task performance and reducing stress. Stress detection systems train a machine-learning model with physiological signals to classify stress levels of unseen data. Unfortunately, individual differences and the time-series nature of physiological signals limit the effectiveness of generalized models and hinder both post-hoc stress detection and real-time monitoring. This study evaluated a personalized stress detection system that selects a personalized subset of features for model training. The system was evaluated post-hoc for real-time deployment. Further, traditional classifiers were assessed for error caused by indirect approximations against a benchmark, optimal probability classifier (Approximate Bayes; ABayes). Healthy participants completed a task with three levels of stressors (low, medium, high), either a complex task in virtual reality (responding to spaceflight emergency fires, n=27) or a simple laboratory-based task (N-back, n=14). Heart rate, blood pressure, electrodermal activity, and respiration were assessed. Personalized features and different interval window sizes were compared. Classification performance was compared for ABayes, support vector machine, decision tree, and random forest. The results demonstrate that a personalized model with time series intervals can classify three stress levels with high accuracy better than a generalized model. However, cross-validation and holdout performance varied for traditional classifiers vs. ABayes, suggesting error from indirect approximations. The selected features changed with windows size and differed between tasks, but blood pressure contributed to the most prominent features. The capability to account for individual difference is a prominent advantage of personalized models, and will likely have a growing presence in future detection systems.

Model constraints independent optimal subsampling probabilities for softmax regression

A prevailing method to alleviate the computational cost is to perform analysis on a subsample of the full data. Optimal subsampling algorithm utilizes non-uniform subsampling probabilities, derived through minimizing the asymptotic mean squared error of the subsample estimator, to acquire a higher estimation efficiency for a given subsample size. The optimal subsampling probabilities for softmax regression have been studied under the baseline constraint which treats one dimension of the multivariate response differently from other dimensions. In this paper, we show that different model constraints lead to different optimal subsampling probabilities, and the summation constraint corresponds to a better subsampling strategy than the baseline constraint in terms of balancing the responses among all categories. Furthermore, we derive the asymptotic distribution of the mean squared prediction error, and minimize its asymptotic expectation to define the optimal subsampling probabilities that are invariant to model constraints. Simulations and a real data example are provided to show the effectiveness of the proposed optimal subsampling probabilities.

Методические основы синтеза моделей развития горных работ для управления процессом воспроизводства очистного фронта угольных шахт

This article presents the results of optimizing the reliability and reliability of the time forecast’s development reserves of the coal mine long wall areas and the preparation time of the face longwall line. A scheme of the reproduction the long wall front process in the form of a decision tree model has been developed. The dependences of the optimal probability of the absence of coal mining losses and the optimal dynamic time reserve on the reliability of longwall and preparatory work and on the ratio of damage in case of shortage, as well as the costs of maintaining the cleaning front, are established. It is shown that when forming a mining development plan, the value of the optimal dynamic time reserve should be determined for each treatment face. It is determined that when providing dynamic redundancy of the process of reproduction of the longwall front, the possibilities of performing degassing and drainage of mineral reserves being prepared for extraction are more fully realized which positively affects the level of reliability and safety of longwall operations.

Classification of High-Concentration Aerosol Phenomena Using Their Physical Properties in Busan, South Korea

High concentrations of aerosols associated with various meteorological phenomena show different physical characteristics. Because these phenomena are differentiated based on the visibility observed with the eyes, the observation may be unreliable. To investigate aerosol physical properties across various meteorological phenomena and develop an algorithm to classify high-concentration aerosol events, each phenomenon was assessed by analyzing the aerosol number and mass concentrations according to particle size observed with an optical particle counter. Furthermore, the optimal probability density function for each phenomenon was derived using the mass concentration by diameter. In addition, total aerosol mass concentrations, geometric mean particle diameters, and geometric standard deviations were calculated. In the coarse mode, the total mass concentration of the Asian dust case was the highest (16.3 μg·cm−3), whereas in the accumulation mode, the haze value was greatest (22.86 μg·cm−3). Average diameters were 4.41 and 0.41 μm in the coarse and accumulation modes, respectively. A classification algorithm for high-concentration aerosol phenomena was proposed based on the determined physical properties, results of simulating long-distance transport using a backward trajectory model, and meteorological conditions. Among the nine verification cases, all the cases coincided with the observation results of the Korea Meteorological Administration.

Unweighted estimation based on optimal sample under measurement constraints

AbstractTo tackle massive data, subsampling is a practical approach to select the more informative data points. However, when responses are expensive to measure, developing efficient subsampling schemes is challenging, and an optimal sampling approach under measurement constraints was developed to meet this challenge. This method uses the inverses of optimal sampling probabilities to reweight the objective function, which assigns smaller weights to the more important data points. Thus, the estimation efficiency of the resulting estimator can be improved. In this paper, we propose an unweighted estimating procedure based on optimal subsamples to obtain a more efficient estimator. We obtain the unconditional asymptotic distribution of the estimator via martingale techniques without conditioning on the pilot estimate, which has been less investigated in the existing subsampling literature. Both asymptotic results and numerical results show that the unweighted estimator is more efficient in parameter estimation.

Optimal scaling of MCMC beyond Metropolis

AbstractThe problem of optimally scaling the proposal distribution in a Markov chain Monte Carlo algorithm is critical to the quality of the generated samples. Much work has gone into obtaining such results for various Metropolis–Hastings (MH) algorithms. Recently, acceptance probabilities other than MH are being employed in problems with intractable target distributions. There are few resources available on tuning the Gaussian proposal distributions for this situation. We obtain optimal scaling results for a general class of acceptance functions, which includes Barker’s and lazy MH. In particular, optimal values for Barker’s algorithm are derived and found to be significantly different from that obtained for the MH algorithm. Our theoretical conclusions are supported by numerical simulations indicating that when the optimal proposal variance is unknown, tuning to the optimal acceptance probability remains an effective strategy.

An Automated Detection of DDoS Attack in Cloud Using Optimized Weighted Fused Features and Hybrid DBN-GRU Architecture

Distributed Denial of Service (DDoS) attack is a severe type of attack. It affects the cloud in terms of loss of repudiation, service outage, financial loss, and data loss. Hence, the optimal feature selection and the hybrid learning-based classifier are developed for evading the normal function disruption in the cloud server. After pre-processing, the resultant data is subjected to the ensemble feature selection. The first feature is selected optimally by the Probability of Fitness-based Billiards-Inspired Optimization (PF-BIO) algorithm, the second feature selection is acquired through the Fisher discriminant, and the last feature selection is carried out by feature correlation with class. Consequently, the resultant of these feature selections is concatenated with the weight parameter to provide weighted fused features, where the weight is getting optimized by the PF-BIO algorithm. Finally, a newly developed hybrid-based learning model, where the Deep Belief Network with the Gated Recurrent Unit (DBN-GRU), in which the hyper parameters are tuned and optimized by the PF-BIO algorithm. Thus, the performance is compared with the existing approaches. Throughout the result analysis, the accuracy of the designed method is 97.05%. Hence, the proposed model proves the efficiency for the detection of DDoS attacks precisely.

Conflict-Free Joint Sampling for Preference Satisfaction through Quantum Interference

Collective decision-making is vital for recent information and communications technologies. In our previous research, we mathematically derived conflict-free joint decision-making that optimally satisfies players' probabilistic preference profiles. However, two problems exist regarding the optimal joint decision-making method. First, as the number of choices increases, the computational cost of calculating the optimal joint selection probability matrix explodes. Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences. Now, it is noteworthy that explicit calculation of the joint probability distribution is not necessarily needed; what is necessary for collective decisions is sampling. This study examines several sampling methods that converge to heuristic joint selection probability matrices that satisfy players' preferences. We show that they can significantly reduce the above problems of computational cost and confidentiality. We analyze the probability distribution each of the sampling methods converges to, as well as the computational cost required and the confidentiality secured. In particular, we introduce two conflict-free joint sampling methods through quantum interference of photons. The first system allows the players to hide their choices while satisfying the players' preferences almost perfectly when they have the same preferences. The second system, where the physical nature of light replaces the expensive computational cost, also conceals their choices under the assumption that they have a trusted third party. This paper has been published in Phys. Rev. Applied 18, 064018 (2022) (DOI: 10.1103/PhysRevApplied.18.064018).

Optimal predictive probability designs for randomized biomarker-guided oncology trials.

Efforts to develop biomarker-targeted anti-cancer therapies have progressed rapidly in recent years. With efforts to expedite regulatory reviews of promising therapies, several targeted cancer therapies have been granted accelerated approval on the basis of evidence acquired in single-arm phase II clinical trials. And yet, in the absence of randomization, patient prognosis for progression-free survival and overall survival may not have been studied under standard of care chemotherapies for emerging biomarker subpopulations prior to the submission of an accelerated approval application. Historical control rates used to design and evaluate emerging targeted therapies often arise as population averages, lacking specificity to the targeted genetic or immunophenotypic profile. Thus, historical trial results are inherently limited for inferring the potential "comparative efficacy" of novel targeted therapies. Consequently, randomization may be unavoidable in this setting. Innovations in design methodology are needed, however, to enable efficient implementation of randomized trials for agents that target biomarker subpopulations. This article proposes three randomized designs for early phase biomarker-guided oncology clinical trials. Each design utilizes the optimal efficiency predictive probability method to monitor multiple biomarker subpopulations for futility. Only designs with type I error between 0.05 and 0.1 and power of at least 0.8 were considered when selecting an optimal efficiency design from among the candidate designs formed by different combinations of posterior and predictive threshold. A simulation study motivated by the results reported in a recent clinical trial studying atezolizumab treatment in patients with locally advanced or metastatic urothelial carcinoma is used to evaluate the operating characteristics of the various designs. Out of a maximum of 300 total patients, we find that the enrichment design has an average total sample size under the null of 101.0 and a total average sample size under the alternative of 218.0, as compared to 144.8 and 213.8 under the null and alternative, respectively, for the stratified control arm design. The pooled control arm design enrolled a total of 113.2 patients under the null and 159.6 under the alternative, out of a maximum of 200. These average sample sizes that are 23-48% smaller under the alternative and 47-64% smaller under the null, as compared to the realized sample size of 310 patients in the phase II study of atezolizumab. Our findings suggest that potentially smaller phase II trials to those used in practice can be designed using randomization and futility stopping to efficiently obtain more information about both the treatment and control groups prior to phase III study.

Optimal Transmission Probabilities of Information and Artificial Noise in Covert Communications

This letter studies a wireless covert communication system, where a jammer generating intermittent artificial noise (AN) is employed to confuse the warden. The transmission probabilities of information and AN are jointly optimized to maximize the communication covertness under a throughput requirement. We first analyse the detection performance of the warden and the throughput of the system, based on which we formulate the optimization problem. Then, we simplify this two-dimensional nonconvex problem to a one-dimensional problem and obtain the closed-form optimal solutions in special cases. The results illustrate that the optimal transmission probabilities of information and AN are 0.5 and 1, respectively, when the throughput requirement is less than a threshold. However, 0.5 and 1 are not optimal when the throughput requirement exceeds the aforementioned threshold, which has not been reported yet.

On the Rate of Convergence of a Classifier Based on a Transformer Encoder

Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able to circumvent the curse of dimensionality provided the a posteriori probability satisfies a suitable hierarchical composition model. Furthermore, the difference between the Transformer classifiers theoretically analyzed in this paper and the ones used in practice today is illustrated by means of classification problems in natural language processing.