02365nas a2200289   4500000000100000000000100001008004100002260001200043653002800055653002100083653000800104653002300112653001300135100003200148700002500180700002200205700002000227700002000247700002300267700002000290245007600310856009500386300000900481490000600490520156500496022001402061       2018                            d  c03/201810aArtificial Intelligence10aMachine Learning10aNLP10aElectronic Records10ae-health1 aIgnacio Hernández Medrano1 aJorge Tello Guijarro1 aCristóbal Belda1 aAlberto Ureña1 aIgnacio Salcedo1 aLuis Espinosa-Anke1 aHoracio Saggion00aSavana: Re-using Electronic Health Records with Artificial Intelligence  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/03/ijimai_4_7_1_pdf_22755.pdf  a8-120 v43 aHealth information grows exponentially (doubling every 5 years), thus generating a sort of inflation of science, i.e. the generation of more knowledge than we can leverage. In an unprecedented data-driven shift, today doctors have no longer time to keep updated. This fact explains why only one in every five medical decisions is based strictly on evidence, which inevitably leads to variability. A good solution lies on clinical decision support systems, based on big data analysis. As the processing of large amounts of information gains relevance, automatic approaches become increasingly capable to see and correlate information further and better than the human mind can. In this context, healthcare professionals are increasingly counting on a new set of tools in order to deal with the growing information that becomes available to them on a daily basis. By allowing the grouping of collective knowledge and prioritizing “mindlines” against “guidelines”, these support systems are among the most promising applications of big data in health. In this demo paper we introduce Savana, an AI-enabled system based on Natural Language Processing (NLP) and Neural Networks, capable of, for instance, the automatic expansion of medical terminologies, thus enabling the re-use of information expressed in natural language in clinical reports. This automatized and precise digital extraction allows the generation of a real time information engine, which is currently being deployed in healthcare institutions, as well as clinical research and management.  a1989-1660