Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding
The evolution of medicine in current times has gone hand in hand with technology where more and more solutions are implemented; those supporting certain medical procedures to serve as base in the field of medical professionals. The process of analyzing data has become an essential resource in the p...
- Autores:
-
Calderón-Vargas, Carlos
Muñoz Castaño, José
Vargas Rincón, María
Rincón Acosta, Víctor Manuel
Mendieta Hernández, Miguel
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- spa
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/31017
- Acceso en línea:
- http://hdl.handle.net/10784/31017
- Palabra clave:
- Web design
Machine learning
training
decision trees
weka
Diseño web
Machine learning
entrenamiento
árboles de decisión
weka
- Rights
- License
- Acceso abierto
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| dc.title.eng.fl_str_mv |
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding |
| dc.title.spa.fl_str_mv |
Aprendizaje profundo para escalas pronósticas en la prescripción a pacientes con riesgo de sangrado gastrointestinal |
| title |
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding |
| spellingShingle |
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding Web design Machine learning training decision trees weka Diseño web Machine learning entrenamiento árboles de decisión weka |
| title_short |
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding |
| title_full |
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding |
| title_fullStr |
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding |
| title_full_unstemmed |
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding |
| title_sort |
Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding |
| dc.creator.fl_str_mv |
Calderón-Vargas, Carlos Muñoz Castaño, José Vargas Rincón, María Rincón Acosta, Víctor Manuel Mendieta Hernández, Miguel |
| dc.contributor.author.spa.fl_str_mv |
Calderón-Vargas, Carlos Muñoz Castaño, José Vargas Rincón, María Rincón Acosta, Víctor Manuel Mendieta Hernández, Miguel |
| dc.contributor.affiliation.spa.fl_str_mv |
Hospital Universitario La Samaritana Hospital Universitario La Samaritana Hospital Universitario La Samaritana Universidad El Bosque Universidad El Bosque |
| dc.subject.keyword.eng.fl_str_mv |
Web design Machine learning training decision trees weka |
| topic |
Web design Machine learning training decision trees weka Diseño web Machine learning entrenamiento árboles de decisión weka |
| dc.subject.keyword.spa.fl_str_mv |
Diseño web Machine learning entrenamiento árboles de decisión weka |
| description |
The evolution of medicine in current times has gone hand in hand with technology where more and more solutions are implemented; those supporting certain medical procedures to serve as base in the field of medical professionals. The process of analyzing data has become an essential resource in the practice of any profession; currently, in hospitals, more specifically in the university hospital La Samaritana. No tool allows the supporting of diagnosis to determine the supply or no, proton pump inhibitors, therefore we have developed an app using a machine learning model, based on decision trees through the weka application, which, after analyzing the data collected, allows the doctor to count with a tool to support this procedure. We hope that with this, doctors can perform an effective analysis before prescribing or not prescribing PPIs. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-12-01 |
| dc.date.available.none.fl_str_mv |
2022-03-23T16:59:33Z |
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2022-03-23T16:59:33Z |
| dc.date.none.fl_str_mv |
2021-12-01 |
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info:eu-repo/semantics/article article info:eu-repo/semantics/publishedVersion publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 |
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Artículo |
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publishedVersion |
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1794-9165 2256-4314 |
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http://hdl.handle.net/10784/31017 |
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1794-9165 2256-4314 |
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http://hdl.handle.net/10784/31017 |
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spa |
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spa |
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https://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/6977 |
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https://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/6977 |
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http://purl.org/coar/access_right/c_abf2 |
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Acceso abierto |
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Acceso abierto http://purl.org/coar/access_right/c_abf2 |
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application/pdf |
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Universidad EAFIT |
| dc.source.spa.fl_str_mv |
Ingeniería y Ciencia, Vol. 17, Núm. 34 (2021) |
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Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees2021-12-012022-03-23T16:59:33Z2021-12-012022-03-23T16:59:33Z1794-91652256-4314http://hdl.handle.net/10784/31017The evolution of medicine in current times has gone hand in hand with technology where more and more solutions are implemented; those supporting certain medical procedures to serve as base in the field of medical professionals. The process of analyzing data has become an essential resource in the practice of any profession; currently, in hospitals, more specifically in the university hospital La Samaritana. No tool allows the supporting of diagnosis to determine the supply or no, proton pump inhibitors, therefore we have developed an app using a machine learning model, based on decision trees through the weka application, which, after analyzing the data collected, allows the doctor to count with a tool to support this procedure. We hope that with this, doctors can perform an effective analysis before prescribing or not prescribing PPIs.La evolución de la medicina en los tiempos actuales ha ido de la mano de la tecnología donde cada vez más se implementan soluciones que apoyan ciertos procedimientos médicos con el objetivo de apoyar el ejercicio de los profesionales de la medicina en su oficio. El procesamiento y análisis de datos se ha convertido en un recurso imprescindible en la práctica de cualquier profesión, actualmente, en los hospitales, más puntualmente en el hospital universitario la samaritana, no se posee una herramienta que permita apoyar el diagnóstico para determinar el suministro o no, de los inhibidores de bombas de protones, por lo tanto hemos desarrollado una aplicación web utilizando un modelo de aprendizaje automático, basado en arboles de decisiones por medio de la aplicación weka, que luego del análisis de los datos recogidos, permita al médico contar con una herramienta para el apoyo de este procedimiento. Esperamos que con la utilización de esta aplicación los médicos puedan realizar un análisis efectivo antes de recetar o no los IBPs.application/pdfspaUniversidad EAFIThttps://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/6977https://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/6977Copyright © 2021 Carlos Calderón-Vargas, José Muñoz Castaño, María Vargas Rincón, Víctor Manuel Rincón AcostaAcceso abiertohttp://purl.org/coar/access_right/c_abf2Ingeniería y Ciencia, Vol. 17, Núm. 34 (2021)Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal BleedingAprendizaje profundo para escalas pronósticas en la prescripción a pacientes con riesgo de sangrado gastrointestinalinfo:eu-repo/semantics/articlearticleinfo:eu-repo/semantics/publishedVersionpublishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Web designMachine learningtrainingdecision treeswekaDiseño webMachine learningentrenamientoárboles de decisiónwekaCalderón-Vargas, CarlosMuñoz Castaño, JoséVargas Rincón, MaríaRincón Acosta, Víctor ManuelMendieta Hernández, MiguelHospital Universitario La SamaritanaHospital Universitario La SamaritanaHospital Universitario La SamaritanaUniversidad El BosqueUniversidad El BosqueIngeniería y Ciencia1734722ORIGINALDeep Learning for Forecast Scales.pdfDeep Learning for Forecast Scales.pdfTexto completo PDFapplication/pdf576316https://repository.eafit.edu.co/bitstreams/4bab7826-efb7-4f89-b037-0df6fdbf9e14/download632b0974bf65651b0e691428cc1ec828MD51Deep Learning for Forecast.htmlDeep Learning for Forecast.htmlTexto completo HTMLtext/html292https://repository.eafit.edu.co/bitstreams/7f99ef05-06b6-47ff-b8ce-888cb66e26be/download445e92611024eca6752cbe87ef4819c6MD53THUMBNAILminaitura-ig_Mesa de trabajo 1.jpgminaitura-ig_Mesa de trabajo 1.jpgimage/jpeg265796https://repository.eafit.edu.co/bitstreams/90e24c04-7c39-4f77-b9df-89c87931ce84/downloadda9b21a5c7e00c7f1127cef8e97035e0MD5210784/31017oai:repository.eafit.edu.co:10784/310172022-05-16 02:45:34.038open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co |