Examinando por Autor "Sanchez, Alberto"

Mostrando 1 - 9 de 9

A streaming data visualization framework for supporting decision-making in the Intensive Care Unit
(Elsevier, 2023) Mohedano-Munoz, Miguel A.; Soguero-Ruiz, Cristina; Mora-Jiménez, Inmaculada; Rubio-Sánchez, Manuel; Álvarez-Rodríguez, Joaquín; Sanchez, Alberto
The number of reporting activities in real time has increased over the last years. This situation has pushed the need for providing real time analysis and visualizations to support decision-making. We propose a visualization framework for exploratory data analysis of multivariate data streams that relies on dimensionality reduction and machine learning techniques for plotting the data in two dimensions. Users can demarcate regions of interest for their study, and use them to make predictions or to decide when to train a new model. The knowledge gained from these visualizations allows users to: (i) characterize the data stream scenario; (ii) track the evolution of a case of interest; and (iii) configure and raise alarms according to the user-defined regions. We illustrate the effectiveness of our proposal through a case study analyzing real-world streaming data to identify patients with multi-drug resistant bacteria when they are in a hospital intensive care unit. Our visualization framework enables the patient follow-up which can allow clinicians to support decisions about the health status evolution of a particular patient. This could provide information for deciding on a particular treatment or whether to isolate patients with a high risk of having multi-drug resistant bacteria since their presence boosts infections in intensive care units.
A virtual reality data visualization tool for dimensionality reduction methods
(Springer, 2024) Morales-Vega, Juan C.; Raya, Laura; Rubio-Sánchez, Manuel; Sanchez, Alberto
In this paper, we present a virtual reality interactive tool for generating and manipulating visualizations for high-dimensional data in a natural and intuitive stereoscopic way. Our tool offers support for a diverse range of dimensionality reduction (DR) algorithms, enabling the transformation of complex data into insightful 2D or 3D representations within an immersive VR environment. The tool also allows users to include annotations with a virtual pen using hand tracking, to assign class labels to the data observations, and to perform simultaneous visualization with other users within the 3D environment to facilitate collaboration.
An autonomic framework for enhancing the quality of data grid services
(Elsevier, 2012-07) Sanchez, Alberto; Mostes, Jesus; S. Perez, Maria; Cortes, Toni
Data grid services have been used to deal with the increasing needs of applications in terms of data volume and throughput. The large scale, heterogeneity and dynamism of grid environments often make management and tuning of these data services very complex. Furthermore, current high-performance I/O approaches are characterized by their high complexity and specific features that usually require specialized administrator skills. Autonomic computing can help manage this complexity. The present paper describes an autonomic subsystem intended to provide self-management features aimed at efficiently reducing the I/O problem in a grid environment, thereby enhancing the quality of service (QoS) of data access and storage services in the grid. Our proposal takes into account that data produced in an I/O system is not usually immediately required. Therefore, performance improvements are related not only to current but also to any future I/O access, as the actual data access usually occurs later on. Nevertheless, the exact time of the next I/O operations is unknown. Thus, our approach proposes a long-term prediction designed to forecast the future workload of grid components. This enables the autonomic subsystem to determine the optimal data placement to improve both current and future I/O operations.
Feature selection based on star coordinates plots associated with Eigenvalue problems
(2021-02-01) Sanchez, Alberto; Raya, Laura; Mohedano-Munoz, Miguel Ángel; Rubio-Sánchez, Manuel
Feature selection consists of choosing a smaller number of variables to work with when analyzing high-dimensional data sets. Recently, several visualization tools, techniques, and feature relevance measures have been developed in order to help users carry out the feature selection. Some of these approaches are based on radial axes methods, where analysts perform backward feature elimination by discarding features that have a low impact on the visualizations. Similarly, in this paper, we propose a new feature relevance measure for star coordinates plots associated with the class of linear dimensionality reduction mappings defined through the solutions of eigenvalue problems, such as linear discriminant analysis or principal component analysis. We show that the approach leads to enhanced feature subsets for class separation or variance maximization in the plots for numerous data sets of the UCI repository. Lastly, in practice, the tool allows analysts to decide which features to discard by examining their relevance and by taking into account previous domain knowledge.
Interactive Visual Clustering and Classification based on Dimensionality Reduction Mappings: A Case Study for Analyzing Patients with Dermatologic Conditions
(2021-06-01) Mohedano-Munoz, Miguel Ángel; Alique-García, Sergio; Rubio-Sánchez, Manuel; Raya, Laura; Sanchez, Alberto
Multidimensional data sets are becoming more frequent in practically all research fields, and require complex data analysis techniques in order to extract knowledge from them. In this paper, we propose an interactive visualization tool for performing exploratory data analysis. The tool combines unsupervised and supervised dimensionality reduction methods, such as linear discriminant analysis, or t-SNE, with clustering and classification techniques. Analysts can use several machine learning methods for extracting data structure, and can group data into clusters interactively or through clustering algorithms. In addition they can visualize projections of the data to evaluate the quality of obtained clusters, and to analyze the performance of classification methods. We have applied this tool to analyze a clinical data set related to patients with dermatologic conditions that are under photodynamic therapy. The analysis allowed medical doctors to identify several clinically interesting patient groups. In addition, clinicians discovered a greater efficacy in the treatment of patients with the photosensitizer 5-aminolaevulinic acid nanoemulsion gel compared to those treated with methyl-5-aminolaevulinate cream.
MDScale: Scalable multi-GPU bonded and short-range molecular dynamics
(Elsevier, 2021) Barreales, Gonzalo Nicolas; Novalbos, Marcos; Otaduy, Miguel A.; Sanchez, Alberto
GPUs have enabled a drastic change to computing environments, making massively parallel computing possible. Molecular dynamics is a perfect candidate problem for massively parallel computing, but to date it has not taken full advantage of multi-GPU environments due to the difficulty of partitioning molecular dynamics problems and exchanging problem data among compute nodes. These difficulties restrict the use of GPUs to only some of the computations in a full molecular dynamics problem, and hence prevent scalability beyond just a few GPUs. This work presents a scalable parallelization solution for the bonded and short-range forces present in a molecular dynamics problem. Together with existing solutions for long-range forces, it enables highly scalable, parallel molecular dynamics on multi-GPU computing environments. Specifically, the proposed solution divides the molecular volume into independent parts assigned to different GPUs, but it maintains a global bond structure that is efficiently exchanged when atoms move across GPUs. We demonstrate close-to-linear speedup of the proposed solution, simulating the dynamics of gigamolecules with 1 billion atoms on a computing environment with 96 GPUs, and obtaining superior performance to the well known molecular dynamics simulator NAMD.
Optimal Axes for Data Value Estimation in Star Coordinates and Radial Axes Plots
(2021-06-29) Rubio-Sánchez, Manuel; Lehmann, Dirk Joachim; Sanchez, Alberto; Rojo-Álvarez, José Luis
Radial axes plots are projection methods that represent high-dimensional data samples as points on a two-dimensional plane. These techniques define mappings through a set of axis vectors, each associated with a data variable, which users can manipulate interactively to create different plots and analyze data from multiple points of view. However, updating the direction and length of an axis vector is far from trivial. Users must consider the data analysis task, domain knowledge, the directions in which values should increase, the relative importance of each variable, or the correlations between variables, among other factors. Another issue is the difficulty to approximate high-dimensional data values in the two-dimensional visualizations, which can hamper searching for data with particular characteristics, analyzing the most common data values in clusters, inspecting outliers, etc. In this paper we present and analyze several optimization approaches for enhancing radial axes plots regarding their ability to represent high-dimensional data values. The techniques can be used not only to approximate data values with greater accuracy, but also to guide users when updating axis vectors or extending visualizations with new variables, since they can reveal poor choices of axis vectors. The optimal axes can also be included in nonlinear plots. In particular, we show how they can be used within RadViz to assess the quality of a variable ordering. The in-depth analysis carried out is useful for visualization designers developing radial axes techniques, or planning to incorporate axes into other visualization methods.
Scaled Radial Axes for Interactive Visual Feature Selection: A Case Study for Analyzing Chronic Conditions
(2018-06-15) Sanchez, Alberto; Soguero-Ruiz, Cristina; Mora-Jiménez, Inmaculada; Rivas-Flores, Francisco Javier; Lehmann, Dirk Joachim; Rubio-Sánchez, Manuel
In statistics, machine learning, and related fields, feature selection is the process of choosing a smaller subset of features to work with. This is an important topic since selecting a subset of features can help analysts to interpret models and data, and to decrease computational runtimes. While many techniques are purely automatic, the data visualization community has produced a number of interactive approaches where users can make decisions taking into account their domain knowledge. In this paper we propose a new visualization technique based on radial axes that allows analysts to perform feature selection effectively, in contrast to previous radial axes methods. This is achieved by employing alternative scaled axes that provide insight regarding the features that have a smaller contribution to the visualizations. Therefore, analysts can use the technique to carry out interactive backwards feature elimination, by discarding the least relevant features according to the information on the plots and their expertise. Our approach can be coupled with any linear dimensionality reduction method, and can be used when performing analyses of cluster structure, correlations, class separability, etc. Specifically, in this paper we focus on combining the proposed technique with methods designed for classification. Lastly, we illustrate the effectiveness of our proposal through a case study analyzing high-dimensional medical chronic conditions data. In particular, clinicians have used the technique for determining the most important features that discriminate between patients with diabetes and high blood pressure.
Visually guided classification trees for analyzing chronic patients
(2020-03-11) Soguero-Ruiz, Cristina; Mora-Jiménez, Inmaculada; Mohedano-Munoz, Miguel Ángel; Rubio-Sánchez, Manuel; de Miguel-Bohoyo, Pablo; Sanchez, Alberto
Background: Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights. Results: In this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses. Conclusions: We propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.