Andoni Beristain

Volume Ray Casting in WebGL

Real-time 3D computer graphics systems usually handle surface description models (i.e. B-Rep representations) and use surface rendering techniques for visualization. Common 3D model formats such as VRML, X3D, COLLADA, U3D (some intended for the Web) are based entirely on polygonal meshes or higher order surfaces. Real-time rendering of polygon models is straightforward and raster render algorithms are implemented in most graphics accelerating hardware. For many years several rendering engines, often via installable browser plug-ins, have been available to support 3D mesh visualization in Web applications.

A stable skeletonization for tabletop gesture recognition

An efficient and stable skeletonization consisting of a Voronoi skeletonization following by a two step pruning is presented. The first pruning step removes Voronoi edges crossing the shape boundary. The second follows a Discrete Curve Evolution approach. Both pruning steps can be done very efficiently because entire Voronoi segments are pruned based on tests on points. The algorithm works in realtime and could be used in a gesture recognition interface for tabletop interfaces.

EMOTION RECOGNITION BASED ON THE ANALYSIS OF FACIAL EXPRESIONS: A SURVEY

Face expression recognition is an active area of research with several fields of applications, ranging from emotion recognition for advanced human computer interaction to avatar animation for the movie industry. This paper presents a review of the state-of-the-art emotion recognition based on the visual analysis of facial expressions. We cover the main technical approaches and discuss the issues related to the gathering of data for the validation of the proposed systems.

Predicting 30-Day Emergency Readmission Risk

Objective: Predicting Emergency Department (ED) readmissions is of great importance since it helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. It is becoming standard procedure to evaluate the risk of ED readmission within 30 days after discharge. Methods. Our dataset is stratified into four groups according to the Kaiser Permanente Risk Stratification Model. We deal with imbalanced data using different approaches for resampling. Feature selection is also addressed by a wrapper method which evaluates feature set importance by the performance of various classifiers trained on them. Results. We trained a model for each scenario and subpopulation, namely case management (CM), heart failure (HF), chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM). Using the full dataset we found that the best sensitivity is achieved by SVM using over-sampling methods (40.62 % sensitivity, 78.71 % specificity and 71.94 accuracy). Conclusions. Imbalance correction techniques allow to achieve better sensitivity performance, however the dataset has not enough positive cases, hindering the achievement of better prediction ability. The arbitrary definition of a threshold-based discretization for measurements which are inherently is an important drawback for the exploitation of the data, therefore a regression approach is considered as future work.

Emergency Department Readmission Risk Prediction: A Case Study in Chile

Short time readmission prediction in Emergency Departments (ED) is a valuable tool to improve both the ED management and the healthcare quality. It helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. As in many other medical domains, patient readmission data is heavily imbalanced, i.e. the minority class is very infrequent, which is a challenge for the construction of accurate predictors using machine learning tools. We have carried computational experiments on a dataset composed of ED admission records spanning more than 100000 patients in 3 years, with a highly imbalanced distribution. We employed various approaches for dealing with this highly imbalanced dataset in combination with different classification algorithms and compared their predictive power for the estimation of the ED readmission probability within 72 h after discharge. Results show that random undersampling and Bagging (RUSBagging) in combination with Random Forest achieves the best results in terms of Area Under ROC Curve (AUC).

Balanced training of a hybrid ensemble method for imbalanced datasets: a case of emergency department readmission prediction

Dealing with imbalanced datasets is a recurrent issue in health-care data processing. Most literature deals with small academic datasets, so that results often do not extrapolate to the large real-life datasets, or have little real-life validity. When minority class sample generation by interpolation is meaningless, the recourse to undersampling the majority class is mandatory in order to reach some acceptable results. Ensembles of classifiers provide the advantage of the diversity of their members, which may allow adaptation to the imbalanced distribution. In this paper, we present a pipeline method combining random undersampling with bootstrap aggregation (bagging) for a hybrid ensemble of extreme learning machines and decision trees, whose diversity improves adaptation to the imbalanced class dataset. The approach is demonstrated on a realistic greatly imbalanced dataset of emergency department patients from a Chilean hospital targeted to predict patient readmission. Computational experiments show that our approach outperforms other well-known classification algorithms.

Gaining Insight from Physical Activity Data using a Similarity-based Interactive Visualization

This paper presents a new interactive visualization approach which aims to help and support the user in gaining insight over his physical activity data. The main novelty of the proposed visualization approach is the representation of similarities in the physical activity patterns in time using data clustering techniques, in addition to the continuous physical activity representation over a circular chart. This grouping of similar activity patterns helps identifying meaningful events or behaviors, combined with the periodicity highlighting circular charts. The user is able to interact with the visualization during the knowledge discovery process by changing the represented time-scale, time-frame and the number of clusters used for the user’s physical activity pattern categorization. Additionally, the proposed visualization approach allows to easily report and store the insights gained during the visual data analysis process, by adding a textual description linked to the particular user tailored visualization configuration which led to that insight.

Innovae Emotional Trainer: an Interactive System to Measure People's Acting Skills

We describe the facial emotion reading and teaching system we have developed as an entertainment and educational tool to be placed in science museums. We describe the computational elements in detail as well as a recognition experiment and the results obtained