2023: Vol. 47, Núm. 1
http://hdl.handle.net/2117/390140
2024-09-14T17:13:31ZInference on the symmetry point-based optimal cut-off point and associated sensitivity and specificity with application to SARS-CoV-2 antibody data
http://hdl.handle.net/2117/397848
Inference on the symmetry point-based optimal cut-off point and associated sensitivity and specificity with application to SARS-CoV-2 antibody data
Franco-Pereira, Alba María; Pardo Llorente, M. Carmen; Nakas, Christos T.; Reiser, Benjamin
In the presence of a continuous response test/biomarker, it is often necessary to identify a cut-off point value to aid binary classification between diseased and non-diseased subjects. The symmetry-point approach which maximizes simultaneously both types of correct classification is one way to determine an optimal cut-off point. In this article, we study methods for constructing confidence intervals independently for the symmetry point and its corresponding sensitivity, as well as respective joint nonparametric confidence regions. We illustrate using data on the generation of antibodies elicited two weeks post-injection after the second dose of the Pfizer/BioNTech vaccine in adult healthcare workers.
2023-12-12T17:30:32ZFranco-Pereira, Alba MaríaPardo Llorente, M. CarmenNakas, Christos T.Reiser, BenjaminIn the presence of a continuous response test/biomarker, it is often necessary to identify a cut-off point value to aid binary classification between diseased and non-diseased subjects. The symmetry-point approach which maximizes simultaneously both types of correct classification is one way to determine an optimal cut-off point. In this article, we study methods for constructing confidence intervals independently for the symmetry point and its corresponding sensitivity, as well as respective joint nonparametric confidence regions. We illustrate using data on the generation of antibodies elicited two weeks post-injection after the second dose of the Pfizer/BioNTech vaccine in adult healthcare workers.Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of R×C tables
http://hdl.handle.net/2117/397846
Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of R×C tables
Pavía, Jose M.; Romero, Rafael
This paper assesses the two current major alternatives for ecological inference, based on a multinomial-Dirichlet Bayesian model and on mathematical programming. Their performance is evaluated in a database made up of almost 2000 real datasets for which the actual cross-distributions are known. The analysis reveals both approaches as complementarity, each one of them performing better in a different area of the simplex space, although with Bayesian solutions deteriorating when the amount of information is scarce. After offering some guidelines regarding the appropriate contexts for employing each one of the algorithms, we conclude with some ideas for exploiting their complementarities.
2023-12-12T17:26:53ZPavía, Jose M.Romero, RafaelThis paper assesses the two current major alternatives for ecological inference, based on a multinomial-Dirichlet Bayesian model and on mathematical programming. Their performance is evaluated in a database made up of almost 2000 real datasets for which the actual cross-distributions are known. The analysis reveals both approaches as complementarity, each one of them performing better in a different area of the simplex space, although with Bayesian solutions deteriorating when the amount of information is scarce. After offering some guidelines regarding the appropriate contexts for employing each one of the algorithms, we conclude with some ideas for exploiting their complementarities.Optimal threshold of data envelopment analysis in bankruptcy prediction
http://hdl.handle.net/2117/397845
Optimal threshold of data envelopment analysis in bankruptcy prediction
Staňková, Michaela; Hampel, David
Data envelopment analysis is not typically used for bankruptcy prediction. However, this paper shows that a correctly set up a model for this approach can be very useful in that context. A superefficiency model was applied to classify bankrupt and actively manufactured companies in the European Union. To select an appropriate threshold, the Youden index and the distance from the corner were used in addition to the total accuracy. The results indicate that selecting a suitable threshold improves specificity visibly with only a small reduction in the total accuracy. The thresholds of the best models appear to be robust enough for predictions in different time and economic sectors.
2023-12-12T17:21:33ZStaňková, MichaelaHampel, DavidData envelopment analysis is not typically used for bankruptcy prediction. However, this paper shows that a correctly set up a model for this approach can be very useful in that context. A superefficiency model was applied to classify bankrupt and actively manufactured companies in the European Union. To select an appropriate threshold, the Youden index and the distance from the corner were used in addition to the total accuracy. The results indicate that selecting a suitable threshold improves specificity visibly with only a small reduction in the total accuracy. The thresholds of the best models appear to be robust enough for predictions in different time and economic sectors.Data science, analytics and artificial intelligence in e-health: trends, applications and challenges
http://hdl.handle.net/2117/397844
Data science, analytics and artificial intelligence in e-health: trends, applications and challenges
Castaneda, Juliana; Calvet, Laura; Benito, Sergio; Tondar, Abtin; Juan, Angel A.
More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines.
2023-12-12T17:18:29ZCastaneda, JulianaCalvet, LauraBenito, SergioTondar, AbtinJuan, Angel A.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines.Transport systems analysis: models and data
http://hdl.handle.net/2117/397841
Transport systems analysis: models and data
Barceló Bugeda, Jaime
Rapid advancements in new technologies, especially information and communication technologies (ICT), have significantly increased the number of sensors that capture data, namely those embedded in mobile devices. This wealth of data has garnered particular interest in analyzing transport systems, with some researchers arguing that the data alone are sufficient enough to render transport models unnecessary. However, this paper takes a contrary position and holds that models and data are not mutually exclusive but rather depend upon each other. Transport models are built upon established families of optimization and simulation approaches, and their development aligns with the scientific principles of operations research, which involves acquiring knowledge to derive modeling hypotheses. We provide an overview of these modeling principles and their application to transport systems, presenting numerous models that vary according to study objectives and corresponding modeling hypotheses. The data required for building, calibrating, and validating selected models are discussed, along with examples of using data analytics techniques to collect and handle the data supplied by ICT applications. The paper concludes with some comments on current and future trends.
2023-12-12T17:14:08ZBarceló Bugeda, JaimeRapid advancements in new technologies, especially information and communication technologies (ICT), have significantly increased the number of sensors that capture data, namely those embedded in mobile devices. This wealth of data has garnered particular interest in analyzing transport systems, with some researchers arguing that the data alone are sufficient enough to render transport models unnecessary. However, this paper takes a contrary position and holds that models and data are not mutually exclusive but rather depend upon each other. Transport models are built upon established families of optimization and simulation approaches, and their development aligns with the scientific principles of operations research, which involves acquiring knowledge to derive modeling hypotheses. We provide an overview of these modeling principles and their application to transport systems, presenting numerous models that vary according to study objectives and corresponding modeling hypotheses. The data required for building, calibrating, and validating selected models are discussed, along with examples of using data analytics techniques to collect and handle the data supplied by ICT applications. The paper concludes with some comments on current and future trends.