Between-within effects in survival models with cross-classified clustering: Application to the evaluation of the effectiveness of medical devices

2016 ◽  
Vol 27 (1) ◽  
pp. 312-319 ◽  
Author(s):  
Guy Cafri ◽  
Juanjuan Fan
Keyword(s):  
Medical Devices ◽  
Survival Data ◽  
Real Data ◽  
Medical Intervention ◽  
Survival Models ◽  
Medical Interventions ◽  
Proposed Model ◽  
Cross Classification ◽  
Better Than

In many medical applications involving observational survival data there will be a cross-classification of doctors and hospitals, as well as an interest in controlling for potentially confounding doctor and hospital effects when evaluating the effectiveness of a medical intervention. In this paper, we propose the use of a between-within model with cross-classified random effects and show through simulation that it performs better than alternative models. A real data example illustrates the application of the proposed model in a study of the survival of hip implants. The proposed model has broad utility in determining the effectiveness of medical interventions.

Socially-Competent Computing Implementing Social Sensor Design

2012 ◽  
Vol 7 (3) ◽  
pp. 61-70 ◽  
Author(s):  
Maya Dimitrova
Keyword(s):  
Communication Networks ◽  
Real Data ◽  
Sensor Design ◽  
Dynamic Interactions ◽  
Medical Interventions ◽  
Human Interactions ◽  
Proposed Model ◽  
Predicting Behavior ◽  
Level Of Understanding ◽  
Social Sensor

The paper presents a conceptual model for social sensor design in socially-competent computing systems. The model is based on theories of social behavior being driven by the underlying attitudes, rather than on models predicting behavior in response to behavior representing people as physical objects in dynamic interactions. It is proposed to increase the ability of the systems to extract relevant features and to achieve better social competence, similar to the kind that is underlying human interactions by implementing algorithms, capable of predicting behavior in response to attitude. The paper presents an account of the social level of understanding human interactions in the context of three application scenarios – multi-hop communication networks, embedded systems for support of medical interventions and information systems supporting educational activities. Patterns of real data are discussed in terms of the proposed model of social sensor design for enhanced socially-competent computing.

Analysis of COVID-19 Fractional Model Pertaining to the Atangana-Baleanu-Caputo Fractional Derivatives

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Roman Ullah ◽  
Muhammad Waseem ◽  
Norhayati Binti Rosli ◽  
Jeevan Kafle
Keyword(s):  
Fractional Derivatives ◽  
Classical Model ◽  
Real Data ◽  
Extended Model ◽  
Model Approximation ◽  
Caputo Fractional Derivatives ◽  
Proposed Model ◽  
Uniqueness Of The Solution ◽  
Ulam Type Stability ◽  
Better Than

The transmission dynamics of a COVID-19 pandemic model with vertical transmission is extended to nonsingular kernel type of fractional differentiation. To study the model, Atangana-Baleanu fractional operator in Caputo sense with nonsingular and nonlocal kernels is used. By using the Picard-Lindel method, the existence and uniqueness of the solution are investigated. The Hyers-Ulam type stability of the extended model is discussed. Finally, numerical simulations are performed based on real data of COVID-19 in Indonesia to show the plots of the impacts of the fractional order derivative with the expectation that the proposed model approximation will be better than that of the established classical model.

scholarly journals CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8231
Author(s):  
Xinyi Hu ◽  
Chunxiang Gu ◽  
Yihang Chen ◽  
Fushan Wei
Keyword(s):  
Traffic Classification ◽  
Traffic Data ◽  
Training Method ◽  
Proposed Model ◽  
Public Dataset ◽  
Basic Characteristics ◽  
Encrypted Traffic ◽  
Closed Environment ◽  
Better Than

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.

scholarly journals Bayesian Inference For The Segmented Weibull Distribution

2019 ◽  
Vol 42 (2) ◽  
pp. 225-243
Author(s):  
Emilio A. Coelho-Barros ◽  
Jorge A. Achcar ◽  
Edson Z. Martinez ◽  
Nasser Davarzani ◽  
Heike I. Grabsch
Keyword(s):  
Monte Carlo ◽  
Survival Data ◽  
Real Data ◽  
Good Alternative ◽  
Change Points ◽  
Data Sets ◽  
Survival Times ◽  
Clinical Value ◽  
Proposed Model ◽  
Censored Observations

In this paper, we introduce a Bayesian approach for segmented Weibull distributions which could be a good alternative to analyze medical survival data in the presence of censored observations and covariates. With the obtained Bayesian estimated change-points we could get an excellent fit of the proposed model to any data sets. With the proposed methodology, it is also possible to identify survival times intervals where a covariate could have significantly different efects when compared to other lifetime intervals, an important point under a clinical view. The obtained Bayesian estimates are obtained using standard Markov Chain Monte Carlo methods. Some examples with real data sets illustrate the proposed methodology and its potential clinical value.

scholarly journals Gravitation Theory Based Model for Multi-Label Classification

2017 ◽  
Vol 12 (5) ◽  
pp. 689
Author(s):  
Liwen Peng ◽  
Yongguo Liu
Keyword(s):  
High Performance ◽  
Text Categorization ◽  
Classification Algorithms ◽  
Universal Gravitation ◽  
The Past ◽  
Proposed Model ◽  
Series Of Experiments ◽  
Music Information ◽  
Better Than

The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure.

scholarly journals Pathological Analysis of Blood Cells Using Deep Learning Techniques

2020 ◽  
Vol 13 ◽  
Author(s):  
Virender Ranga ◽  
Shivam Gupta ◽  
Priyansh Agrawal ◽  
Jyoti Meena
Keyword(s):  
Deep Learning ◽  
Blood Cells ◽  
Well Being ◽  
Input Image ◽  
Medical Sciences ◽  
Human Errors ◽  
Deep Dive ◽  
Proposed Model ◽  
Better Than

Introduction: The major area of work of pathologists is concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. But this method suffers from a lot of problems, like there is no standard way of diagnosing, human errors and it puts a heavy load on the laboratory men to diagnose such a large number of slides daily. Method: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to deep dive into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, testing loss for proposed model is plotted using matplotlib and trends. Discussion: The performance of proposed model is better than existing standard architectures and other work done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory men. This can also in turn help pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the images with an accuracy of 95.24%. This accuracy is better than standard architectures.. Further it can be seen that the proposed neural network performs better than present related works carried by various researchers.

scholarly journals AN APPLICATION OF THE POISSON-WEIBULL MODEL FOR CENSURED DATA

2021 ◽  
Vol 39 (4) ◽  
pp. 505-521
Author(s):  
Valdemiro Piedade VIGAS ◽  
Fábio PRATAVIERA ◽  
Giovana Oliveira SILVA
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Survival Data ◽  
Real Data ◽  
Weibull Model ◽  
Maximum Likelihood Estimates ◽  
Data Set ◽  
Kaplan Meier ◽  
Proposed Model ◽  
The Mean

In this paper, we proposed the Poisson-Weibull distribution for the modeling of survival data. The motivation to study this model since, in addition to generalizing the Weibull distribution, which is widely used in several areas of knowledge among them the Survival and Reliability analysis, it presents great exibility in the forms of the hazard function. The Poisson-Weibull distribution was created in a composition of discrete and continuous distributions where there is no information about which factor was responsible for the component failure, only the minimum lifetime value among all risks is observed. The maximum likelihood approach was used to estimate the parameters of the model. Also was conducted a simulation study to examine the mean, the bias, and the root of the mean square error of the maximum likelihood estimates of the proposed model according to the censoring percentages and sample sizes. The model selection criteria were also applied, in addition to graphic techniques such as TTT-Plot and Kaplan-Meier. Application to the real data set was used to illustrate the usefulnessof the distribution.

scholarly journals Proportional Hazard Birnbaum-Saunders Distribution With Application to the Survival Data Analysis

2016 ◽  
Vol 39 (1) ◽  
pp. 129-147
Author(s):  
Germán Moreno Arenas ◽  
Guillermo Martínez Flórez ◽  
Carlos Barrera Causil
Keyword(s):  
Survival Data ◽  
Likelihood Estimation ◽  
Real Data ◽  
Model Parameters ◽  
Lifetime Data ◽  
Proportional Hazard ◽  
Data Set ◽  
Proposed Model ◽  
Log Linear ◽  
Skewness And Kurtosis

<p>Birnbaum Saunders (1969b) used a probability distribution to explain the lifetime data and stress produced in materials. Based on this distribution, we propose a generalization of the Birnbaum-Saunders distribution, referred to as the proportional hazard Birnbaum-Saunders distribution, which includes a new parameter that provides more flexibility in terms of skewness and kurtosis than existing models. We derive the main properties of the model. We discuss maximum likelihood estimation of the model parameters. As a natural step, we define the log-linear proportional hazard Birnbaum-Saunders regression model. An empirical application to a real data set is presented in order to illustrate the usefulness of the proposed model. The results showed that the proportional hazard Birnbaum-Saunders model can be used quite effectively in analyzing survival data, reliability problems and fatigue life studies.</p>

scholarly journals HYPOTHESIS TESTING FOR THE PARAMETERS OF LOG-LOGISTIC REGRESSION MODEL WITH LEFT-TRUNCATED AND RIGHT-CENSORED SURVIVAL DATA

2018 ◽  
Vol 80 (3) ◽  
Author(s):  
Wan Nur Atikah Wan Mohd Adnan ◽  
Jayanthi Arasan
Keyword(s):  
Survival Data ◽  
Logistic Model ◽  
Right Censoring ◽  
Parameter Estimates ◽  
Truncation Level ◽  
Censored Survival Data ◽  
Proposed Model ◽  
The Right ◽  
Data Log ◽  
Better Than

Left-truncation and right-censoring (LTRC) arise naturally in lifetime data. Data may be left-truncated due to a limitation in the study design. Failure of a unit is observed only if it fails after a certain period. Usually, the units under study may not be followed until all of them have failed but the study has to be stopped at a certain time. This introduces the right censoring into the survival data.  Log-logistic model is extended to accommodate the left-truncated and right-censored survival data.  The bias, standard error (SE), and root mean square error (RMSE) of the parameter estimates are computed to evaluate the performance of the model at different sample sizes, censoring proportion (CP), and truncation level (TL). The results show that the SE of the parameter estimates increase as the truncation level (TL) and censoring proportion (CP) increase. Having low and high TL (5% and 15%) in the data, the graphs clearly show that the empirical power of both tests increases with the increase of TL for parameter and . The SE and RMSE also decrease as the sample size increases. Following that, power analysis is conducted via simulation to compare the performance of hypothesis tests based on the Wald and Likelihood Ratio (LR) for the parameters. The results clearly indicate that the Wald performs slightly better than the LR when dealing with the proposed model.

scholarly journals An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 480 ◽  
Author(s):  
César Gil ◽  
Javier Parra-Arnau
Keyword(s):  
Risk Analysis ◽  
Common Knowledge ◽  
Satisfactory Performance ◽  
Detection Technology ◽  
Proposed Model ◽  
Powerful Means ◽  
The Status ◽  
Game Theoretic ◽  
Better Than

The Internet, with the rise of the IoT, is one of the most powerful means of propagating a terrorist threat, and at the same time the perfect environment for deploying ubiquitous online surveillance systems.This paper tackles the problem of online surveillance, which we define as the monitoring by a security agency of a set of websites through tracking and classification of profiles that are potentially suspected of carrying out terrorist attacks. We conduct a theoretical analysis in this scenario that investigates the introduction of automatic classification technology compared to the status quo involving manual investigation of the collected profiles. Our analysis starts examining the suitability of game-theoretic-based models for decision-making in the introduction of this technology. We propose an adversarial-risk-analysis (ARA) model as a novel way of approaching the online surveillance problem that has the advantage of discarding the hypothesis of common knowledge. The proposed model allows us to study the rationality conditions of the automatic suspect detection technology, determining under which circumstances it is better than the traditional human-based approach. Our experimental results show the benefits of the proposed model. Compared to standard game theory, our ARA-based model indicates in general greater prudence in the deployment of the automatic technology and exhibits satisfactory performance without having to relax crucial hypotheses such as common knowledge and therefore subtracting realism from the problem, although at the expense of higher computational complexity.

Sign in / Sign up

Export Citation Format

Share Document