Discretization Of Continuous Variables Research Articles

Common methodological pitfalls in ICI pneumonitis risk prediction studies.

Pneumonitis is one of the most common adverse events induced by the use of immune checkpoint inhibitors (ICI), accounting for a 20% of all ICI-associated deaths. Despite numerous efforts to identify risk factors and develop predictive models, there is no clinically deployed risk prediction model for patient risk stratification or for guiding subsequent monitoring. We believe this is due to systemic suboptimal approaches in study designs and methodologies in the literature. The nature and prevalence of different methodological approaches has not been thoroughly examined in prior systematic reviews. The PubMed, medRxiv and bioRxiv databases were used to identify studies that aimed at risk factor discovery and/or risk prediction model development for ICI-induced pneumonitis (ICI pneumonitis). Studies were then analysed to identify common methodological pitfalls and their contribution to the risk of bias, assessed using the QUIPS and PROBAST tools. There were 51 manuscripts eligible for the review, with Japan-based studies over-represented, being nearly half (24/51) of all papers considered. Only 2/51 studies had a low risk of bias overall. Common bias-inducing practices included unclear diagnostic method or potential misdiagnosis, lack of multiple testing correction, the use of univariate analysis for selecting features for multivariable analysis, discretization of continuous variables, and inappropriate handling of missing values. Results from the risk model development studies were also likely to have been overoptimistic due to lack of holdout sets. Studies with low risk of bias in their methodology are lacking in the existing literature. High-quality risk factor identification and risk model development studies are urgently required by the community to give the best chance of them progressing into a clinically deployable risk prediction model. Recommendations and alternative approaches for reducing the risk of bias were also discussed to guide future studies.

A comparison of methods for discretizing continuous variables in Bayesian Networks

Bayesian Networks (BNs) are an increasingly popular method for modelling environmental systems. The discretization of continuous variables is often required to use BNs. There are three main methods of discretization; manual, unsupervised, and supervised. Here, we compare and demonstrate each approach with a BN that predicts coastal erosion. Results reveal that supervised discretization methods produced BNs of the highest average predictive skill (73.8%), followed by manual discretization (69.0%) and unsupervised discretization (64.8%). However, each method has specific advantages that may make them more suitable for particular applications. Manual methods can produce physical meaningful BNs, which is favorable in environmental modelling. Supervised methods can autonomously and optimally discretize variables and may be preferred when predictive skill is a modelling priority. Unsupervised methods are computationally simple and versatile. The optimal discretization scheme should consider both the performance and practicality of the scheme.

A Performance Evaluation Algorithm of Stochastic Hybrid Systems Based on Fuzzy Health Degree and Its Application to Quadrotors

A stochastic hybrid system (SHS) is a type of model describing real-world systems, who are both dynamic and hybrid. System performance evaluation is a key technology related to mission accomplishment and safety, where a rational selection of performance indicators is an important guarantee of truly and accurately evaluating system performance. This paper proposes a performance evaluation algorithm of SHS. First, the concept of fuzzy health degree is proposed and the SHS model studied in this paper is established. Then, the procedure of the algorithm is presented in detail, including hybrid state estimation based on a modified interacting-multiple-model algorithm, discretization of continuous variables, and quantitative calculation of fuzzy health degree. In order to validate the effectiveness of the proposed algorithm, an experiment of a quadrotor with sensor anomalies is made where the system performance is quantitatively described by using the fuzzy health degree. Comparative studies are also made and discussed.

FOREST COVER ANALYSIS THROUGH THE WEIGHTS OF EVIDENCE METHOD IN THE CAMPANHA OCIDENTAL REGION - RS (BRAZIL)

ABSTRACT The uncontrolled expansion of human activities may lead to a reduction in vegetation cover, an increase in erosion processes and soil sealing. The aim of this study is to examine forest cover using the Weights of evidence method based on land use maps between the years of 1996 and 2011 in the micro-region of the Campanha Ocidental located in the state of Rio Grande do Sul (Brazil). The spatial database was constructed in SPRING software (version 5.2.1) based on LANDSAT 5 images, which were georeferenced and classified. Geophysical and socioeconomic variables were included in the database for further analysis in Dinamica EGO software (version 2.2.8). In order to parameterize the probabilistic model for the analysis of the dynamics of forest cover change, we calculated the percentage of class change through transition matrices; calculated the intervals for discretization of continuous variables; calculated the weights of evidence (W+); analyzed the correlation between the weights of the input variables for all transitions; simulated future scenarios and; validated the simulated final map based on the historical map. This model was adequate for understanding the variables that most contribute to forest cover change in the region. The results showed that the emergence of new forest areas was influenced by hypsometry, distance to sandy lands and per capita income, while deforestation by rural population and distance to the sandy lands.

Comparative analysis of discretization methods in Bayesian networks

A key step in implementing Bayesian networks (BNs) is the discretization of continuous variables. There are several mathematical methods for constructing discrete distributions, the implications of which on the resulting model has not been discussed in literature. Discretization invariably results in loss of information, and both the discretization method and the number of intervals determines the level of such loss. We designed an experiment to evaluate the impact of commonly used discretization methods and number of intervals on the developed BNs. The conditional probability tables, model predictions, and management recommendations were compared and shown to be different among models. However, none of the models did uniformly well in all comparison criteria. As we cannot justify using one discretization method against others, we recommend caution when discretization is used, and a verification process that includes evaluating alternative methods to ensure that the conclusions are not an artifact of the discretization approach.

Supervised discretization can discover risk groups in cancer survival analysis

Discretization of continuous variables is a common practice in medical research to identify risk patient groups. This work compares the performance of gold-standard categorization procedures (TNM+A protocol) with that of three supervised discretization methods from Machine Learning (CAIM, ChiM and DTree) in the stratification of patients with breast cancer. The performance for the discretization algorithms was evaluated based on the results obtained after applying standard survival analysis procedures such as Kaplan–Meier curves, Cox regression and predictive modelling. The results show that the application of alternative discretization algorithms could lead the clinicians to get valuable information for the diagnosis and outcome of the disease. Patient data were collected from the Medical Oncology Service of the Hospital Clínico Universitario (Málaga, Spain) considering a follow up period from 1982 to 2008.

Hybrid Method for Quantifying and Analyzing Bayesian Belief Nets

AbstractBayesian belief nets (BBNs) have become a popular tool for specifying high‐dimensional probabilistic models. Commercial tools with an advanced graphical user interface that support BBNs construction and inference are available. Thus, building and working with BBNs is very efficient as long as one is not forced to quantify complex BBNs. A high assessment burden of discrete BBNs is often caused by the discretization of continuous variables. Until recently, continuous BBNs were restricted to the joint normal distribution. We present the ‘copula–vine’ approach to continuous BBNs. This approach is quite general and allows traceable and defendable quantification methods, but it comes at a price: these BBNs must be evaluated by Monte Carlo simulation. Updating such a BBN requires re‐sampling the whole structure. The advantages of fast updating algorithms for discrete BBNs are decisive. A hybrid method advanced here samples the continuous BBN once, and then discretizes this so as to enable fast updating. This combines the reduced assessment burden and modelling flexibility of the continuous BBNs with the fast updating algorithms of discrete BBNs. Sampling large complex structures only once can still involve time consuming numerical calculations. Therefore a new sampling protocol based on normal vines is developed. Normal vines are used to realize the dependence structure specified via (conditional) rank correlations on the continuous BBN. We will emphasize the advantages of this method by means of examples. Copyright © 2006 John Wiley & Sons, Ltd.

Fast modelling of roll stack behaviour for control applications

A fast model of roll stack deformation is presented. Appropriate simplified structural models are chosen by careful consideration of the application of general theories. Discretization of continuous variables allows the use of matrix-based weighting functions in describing component deformation and leads to a vectorized solution. All model non-linearities are considered, with particular attention given to the variable region of contact between adjacent rolls in the stack. A novel and efficient ‘binary choice matrix’ method is developed to describe the uncertainty in defining this region. This leads to a more efficient solution than previously published related models. Opportunities for future extensions are discussed.

Improving scoring techniques in banking applications

AbstractScoring techniques are largely used today in banking applications, for marketing as well as risk assessment or control purposes. The most widely used method is Fisher's discriminatory analysis. We discuss here this method from a technical standpoint, showing its weaknesses and then propose a simpler and more general alternative. We first make an original presentation of the various scoring techniques and their basic principles. In particular, we provide a detailed description of Fisher's method, insisting on sensitive steps such as the discretization of continuous variables which is responsible for the nonlinearity of the model. We then go on to propose a new method that is completely linear and improves scoring performance. This technique is easy to explain, compute and use in real applications. Finally, we compare the performance of this new method with that of traditional techniques and of the more up‐to‐date neural nets method.