Enrique Puertas

Concept indexing for automated text categorization

In this paper we explore the potential of concept indexing with WordNet synsets for Text Categorization, in comparison with the traditional bag of words text representation model. We have performed a series of experiments in which we also test the possibility of using simple yet robust disambiguation methods for concept indexing, and the effectiveness of stoplist-filtering and stemming on the SemCor semantic concordance. Results are not conclusive yet promising.

Detection and visualization of potential traffic hotspots in urban environments

In this paper we present a novel contribution work, describing a system that helps to detect urban areas with high density of incidents or risk situations. Our system has a perception and pre-collision system that feed an expert system aimed at detecting dangerous situations. All incidents gathered by the vehicles are sent to a global repository, and its data is processed for detecting urban hotspots where the density of incidents is very high. That information can be useful for city councils, allowing them to address these areas with a high concentration of risk situations.

Distributed Big Data Techniques for Health Sensor Information Processing

Recent advances in wireless sensors technology applied to e-health allow the development of “personal medicine” concept, whose main goal is to identify specific therapies that make safe and effective individualized treatment of patients based, for example, in health status remote monitoring. Also the existence of multiple sensor devices in Hospital Units like ICUs (Intensive Care Units) constitute a big source of data, increasing the volume of health information to be analyzed in order to detect or predict abnormal situations in patients. In order to process this huge volume of information it is necessary to use Big Data and IoT technologies. In this paper, we present a general approach for sensor’s information processing and analysis based on Big Data concepts and to describe the use of common tools and techniques for storing, filtering and processing data coming from sensors in an ICU using a distributed architecture based on cloud computing. The proposed system has been developed around Big Data paradigms using bio-signals sensors information and machine learning algorithms for prediction of outcomes.

Health Sensors Information Processing and Analytics Using Big Data Approaches

In order of maintain the sustainability of the public health systems it is necessary to develop new medical applications to reduce the affluence of chronic and dependent people to care centers and enabling the management of chronic diseases outside institutions Recent advances in wireless sensors technology applied to e-health allow the development of “personal medicine” concept, whose main objective is to identify specific therapies that make safe and effective individualized treatment of patients based for example in remote monitoring. The volume of health information to manage, including data from medical and biological sensors make necessary to use Big Data and IoT concepts for an adequate treatment of this kind of information. In this paper we present a general approach for sensor’s information processing and analytics based on Big Data concepts.

Big Data Processing of Bio-signal Sensors Information for Self-Management of Health and Diseases

These last years developed countries and specially European countries are characterized by aging population and economical crisis, as a consequence, the funds dedicated to social services has been diminished specially those dedicated to healthcare, is then desirable to optimize the costs of public and private healthcare systems reducing the affluence of chronic and dependant people to care centers and enabling the management of chronic diseases outside institutions. It is necessary to streamline the health system resources leading to the development of new medical services based on telemedicine and biomedical sensors. New health applications based on remote monitoring will significantly increasing the volume of health information to store, manage and analyze, including heterogeneous data coming from medical records and biomedical sensors. The Big Data and IoT concepts and techniques offer an integrated approach to develop a suitable architecture for an adequate treatment of this kind of information.

A Framework for Urban Traffic Hotspots Detection

The present paper deals with partial development of SAMPLER Framework, based on an on-board perception and pre-collision system as well as an event data recorder (EDR), aimed at identifying risky traffic situations in urban areas. Information relayed to the detected risky traffic situations are then exploited to find out remarkable urban traffic hotspots visualizing them as layers on a digital map.

Learning tool for medicine students based on biomedical named entity recognition and Linked Open Data

In this paper, we present a computer learning tool aimed at helping medical undergraduate students in the process of understanding biomedical concepts in clinical case histories. The system automatically detects concepts and biomedical entities in the text and presents them to the user linked to rich content extracted from Linked Open Data resources. A student can paste a clinical case text and automatically explore relevant information about biomedical topics such as medical conditions, symptoms, treatments, drugs and other biomedical literature.