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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad Internacional de La Rioja (UNIR)
2018-03-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.ijimai.org/journal/node/1619 |
| _version_ | 1818544736189808640 |
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| author | Ignacio Hernández Medrano Jorge Tello Guijarro Cristóbal Belda Alberto Ureña Ignacio Salcedo Luis Espinosa-Anke Horacio Saggion |
| author_facet | Ignacio Hernández Medrano Jorge Tello Guijarro Cristóbal Belda Alberto Ureña Ignacio Salcedo Luis Espinosa-Anke Horacio Saggion |
| author_sort | Ignacio Hernández Medrano |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T22:52:20Z |
| format | Article |
| id | doaj.art-1cbe5a7fe44742d798b1dec20524e36b |
| institution | Directory Open Access Journal |
| issn | 1989-1660 1989-1660 |
| language | English |
| last_indexed | 2024-12-11T22:52:20Z |
| publishDate | 2018-03-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-1cbe5a7fe44742d798b1dec20524e36b2022-12-22T00:47:24ZengUniversidad 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 |
| 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 |
| title | 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_short | Savana: Re-using Electronic Health Records with Artificial Intelligence |
| title_sort | savana re using electronic health records with artificial intelligence |
| topic | Artificial Intelligence e-health Electronic Records Machine Learning NLP |
| url | http://www.ijimai.org/journal/node/1619 |
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