Electronic Health Records Exploitation Using Artificial Intelligence Techniques
The exploitation of electronic health records (EHRs) has multiple utilities, from predictive tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning mo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Proceedings |
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| Online Access: | https://www.mdpi.com/2504-3900/54/1/60 |
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| author | Carla Guerra Tort Vanessa Aguiar Pulido Victoria Suárez Ulloa Francisco Docampo Boedo José Manuel López Gestal Javier Pereira Loureiro |
| author_facet | Carla Guerra Tort Vanessa Aguiar Pulido Victoria Suárez Ulloa Francisco Docampo Boedo José Manuel López Gestal Javier Pereira Loureiro |
| author_sort | Carla Guerra Tort |
| collection | DOAJ |
| description | The exploitation of electronic health records (EHRs) has multiple utilities, from predictive tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning model from EHR data to make predictions about patients. Specifically, we will focus our analysis on patients suffering from respiratory problems. Then, we will try to predict whether those patients will have a relapse in less than 6, 12 or 18 months. The main objective is to identify the characteristics that seem to increase the relapse risk. At the same time, we propose an exploratory analysis in search of hidden patterns among data. These patterns will help us to classify patients according to their specific conditions for some clinical variables. |
| first_indexed | 2024-03-10T16:28:18Z |
| format | Article |
| id | doaj.art-144fb8be42c5497084a1595d64ec6328 |
| institution | Directory Open Access Journal |
| issn | 2504-3900 |
| language | English |
| last_indexed | 2024-03-10T16:28:18Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj.art-144fb8be42c5497084a1595d64ec63282023-11-20T13:01:23ZengMDPI AGProceedings2504-39002020-09-015416010.3390/proceedings2020054060Electronic Health Records Exploitation Using Artificial Intelligence TechniquesCarla Guerra Tort0Vanessa Aguiar Pulido1Victoria Suárez Ulloa2Francisco Docampo Boedo3José Manuel López Gestal4Javier Pereira Loureiro5CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, SpainDepartment of Computer Science, University of Miami, Coral Gables, FL 33146, USAInstitute for Biomedical Research of A Coruña (INIBIC)-Fundación Profesor Novoa Santos, 15006 A Coruña, SpainInstituto Médico Quirúrgico San Rafael, 15009 A Coruña, SpainInstituto Médico Quirúrgico San Rafael, 15009 A Coruña, SpainCITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, SpainThe exploitation of electronic health records (EHRs) has multiple utilities, from predictive tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning model from EHR data to make predictions about patients. Specifically, we will focus our analysis on patients suffering from respiratory problems. Then, we will try to predict whether those patients will have a relapse in less than 6, 12 or 18 months. The main objective is to identify the characteristics that seem to increase the relapse risk. At the same time, we propose an exploratory analysis in search of hidden patterns among data. These patterns will help us to classify patients according to their specific conditions for some clinical variables.https://www.mdpi.com/2504-3900/54/1/60electronic health record (EHR)Artificial Intelligence (AI)relapserespiratory diseases |
| spellingShingle | Carla Guerra Tort Vanessa Aguiar Pulido Victoria Suárez Ulloa Francisco Docampo Boedo José Manuel López Gestal Javier Pereira Loureiro Electronic Health Records Exploitation Using Artificial Intelligence Techniques Proceedings electronic health record (EHR) Artificial Intelligence (AI) relapse respiratory diseases |
| title | Electronic Health Records Exploitation Using Artificial Intelligence Techniques |
| title_full | Electronic Health Records Exploitation Using Artificial Intelligence Techniques |
| title_fullStr | Electronic Health Records Exploitation Using Artificial Intelligence Techniques |
| title_full_unstemmed | Electronic Health Records Exploitation Using Artificial Intelligence Techniques |
| title_short | Electronic Health Records Exploitation Using Artificial Intelligence Techniques |
| title_sort | electronic health records exploitation using artificial intelligence techniques |
| topic | electronic health record (EHR) Artificial Intelligence (AI) relapse respiratory diseases |
| url | https://www.mdpi.com/2504-3900/54/1/60 |
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