Big data and IoT for chronic patients monitoring

Developed countries are characterized by aging population and economical crisis, so it is desirable to reduce the costs of public and private healthcare systems. It is necessary to streamline the health system resources leading to the development of new medical services based on telemedicine, remote monitoring of chronic patients, personalized health services, new services for dependants, etc. New medical applications based on remote monitoring will significantly increasing the volume of health information to manage, including data from medical and biological sensors, is then necessary process this huge volume of data using techniques from Big Data. In this paper we propose one potential solution for creating those new services, based on Big Data processing and vital signs monitoring.Ministerio de Industria, Turismo y Comercio (TSI-020100-2011-83); Ministerio de Ciencia e Innovación (TIN-2009-14057-C03-01).0.339 SJR (2014) Q2, 102/234 Computer science (miscellaneous); Q4, 94/120 Theoretical computer scienceUE

Modeling Relations and Their Mentions without Labeled Text

Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related entities is an expression of the given relation. Here we argue that this leads to noisy patterns that hurt precision, in particular if the knowledge base is not directly related to the text we are working with. We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decision whether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB. We apply our approach to extract relations from the New York Times corpus and use Freebase as knowledge base. When compared to a state-of-the art approach for relation extraction under distant supervision, we achieve 31% error reduction