Savana: Re-using Electronic Health Records with Artificial Intelligence

Health 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 ev...

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Main Authors: Ignacio Hernández Medrano, Jorge Tello Guijarro, Cristóbal Belda, Alberto Ureña, Ignacio Salcedo, Luis Espinosa-Anke, Horacio Saggion
Format: Article
Language:English
Published: Universidad Internacional de La Rioja (UNIR) 2018-03-01
Series:International Journal of Interactive Multimedia and Artificial Intelligence
Subjects:
NLP
Online Access:http://www.ijimai.org/journal/node/1619
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spelling doaj-1cbe5a7fe44742d798b1dec20524e36b2020-11-25T00:45:53ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602018-03-01478210.9781/ijimai.2018.472ijimai.2018.472Savana: Re-using Electronic Health Records with Artificial IntelligenceIgnacio Hernández MedranoJorge Tello GuijarroCristóbal BeldaAlberto UreñaIgnacio SalcedoLuis Espinosa-AnkeHoracio SaggionHealth 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.http://www.ijimai.org/journal/node/1619Artificial Intelligencee-healthElectronic RecordsMachine LearningNLP
collection DOAJ
language English
format Article
sources DOAJ
author Ignacio Hernández Medrano
Jorge Tello Guijarro
Cristóbal Belda
Alberto Ureña
Ignacio Salcedo
Luis Espinosa-Anke
Horacio Saggion
spellingShingle Ignacio Hernández Medrano
Jorge Tello Guijarro
Cristóbal Belda
Alberto Ureña
Ignacio Salcedo
Luis Espinosa-Anke
Horacio Saggion
Savana: Re-using Electronic Health Records with Artificial Intelligence
International Journal of Interactive Multimedia and Artificial Intelligence
Artificial Intelligence
e-health
Electronic Records
Machine Learning
NLP
author_facet Ignacio Hernández Medrano
Jorge Tello Guijarro
Cristóbal Belda
Alberto Ureña
Ignacio Salcedo
Luis Espinosa-Anke
Horacio Saggion
author_sort Ignacio Hernández Medrano
title Savana: Re-using Electronic Health Records with Artificial Intelligence
title_short Savana: Re-using Electronic Health Records with Artificial Intelligence
title_full Savana: Re-using Electronic Health Records with Artificial Intelligence
title_fullStr Savana: Re-using Electronic Health Records with Artificial Intelligence
title_full_unstemmed Savana: Re-using Electronic Health Records with Artificial Intelligence
title_sort savana: re-using electronic health records with artificial intelligence
publisher Universidad Internacional de La Rioja (UNIR)
series International Journal of Interactive Multimedia and Artificial Intelligence
issn 1989-1660
1989-1660
publishDate 2018-03-01
description Health 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.
topic Artificial Intelligence
e-health
Electronic Records
Machine Learning
NLP
url http://www.ijimai.org/journal/node/1619
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