Una introducción a las aplicaciones de la inteligencia artificial en Medicina: Aspectos históricos
| dc.contributor.author | Arias, Victor | |
| dc.contributor.author | Salazar, Juan | |
| dc.contributor.author | Gariciano, Carlos | |
| dc.contributor.author | Contreras, Julio | |
| dc.contributor.author | Chacón, Gerardo | |
| dc.contributor.author | Chacín-González, Maricarmen | |
| dc.contributor.author | Añez, Roberto | |
| dc.contributor.author | Rojas, Joselyn | |
| dc.contributor.author | Bermúdez-Pirela, Valmore | |
| dc.date.accessioned | 2020-01-30T14:39:58Z | |
| dc.date.available | 2020-01-30T14:39:58Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | En un sentido amplio la inteligencia artificial y el aprendizaje automático se ha aplicado a los datos médicos desde los inicios de la informática dado el profundo arraigo de esta área en la innovación, pero los últimos años han sido testigo de una generación cada vez mayor de datos relacionados con las ciencias de la salud, cuestión que ha dado nacimiento a un nuevo campo de las ciencias de la computación llamado big data. Los datos médicos a gran escala (en forma de bases de datos estructuradas y no estructuradas) si son apropiadamente adquiridos e interpretados pueden generar grandes beneficios al reducir los costos y los tiempos del servicio de salud, pero también podrían servir para predecir epidemias, mejorar los esquemas terapéuticos, asesorar a médicos en lugares remotos y mejorar la calidad de vida. Los algoritmos de deep learning son especialmente útiles para manejar esta gran cantidad de datos complejos, poco documentados y generalmente no estructurados; todo esto debido a que el deep learning puede irrumpir al crear modelos que descubren de forma automática las características principales, así como las que mejor predicen el comportamiento de otras variables dentro de una gran cantidad de datos complejos. En el futuro, la relación hombre-máquina en biomedicina será más estrecha; mientras que la máquina se encargará de tareas de extracción, limpieza y búsquedas de correlaciones, el médico se concentraría en interpretar estas correlaciones y buscar nuevos tratamientos que mejoren la atención y en última instancia la calidad de vida del paciente. | spa |
| dc.description.abstract | In a broad sense, artificial intelligence and machine learning have been applied to medical data since the beginning of computing given the deep roots of this area in innovation, but recent years have witnessed an increasing generation of data related to health sciences, an issue that has given birth to a new field of computer science called big data. Large-scale medical data (in the form of structured and unstructured databases) if properly acquired and interpreted can generate great benefits by reducing costs and times of health service, but could also serve to predict epidemics, improve therapeutic schemes, advise doctors in remote places and improve the quality of life. The deep learning algorithms are especially useful to deal with this large amount of complex, poorly documented and generally unstructured data, all this because deep learning can break when creating models that automatically discover the predictive characteristics of a large amount of complex data. In the future, the human-machine relationship in the medical evaluation will be narrower and complex; while the machine would be responsible for extraction, cleaning and assisted searches, the physician will be concentrate on both, data interpretation and the best treatment option, improving the patient´s attention and ultimately, quality of life. | eng |
| dc.format.mimetype | spa | |
| dc.identifier.issn | 18564550 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/4605 | |
| dc.language.iso | spa | spa |
| dc.publisher | Cooperativa servicios y suministros 212518 RS | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source | Revista Latinoamericana de Hipertensión | spa |
| dc.source | Vol. 14, No. 5 (2019) | spa |
| dc.source.uri | http://www.revhipertension.com/rlh_5_2019/13_una_introduccion.pdf | |
| dc.subject | Inteligencia artificial | spa |
| dc.subject | Innovación | spa |
| dc.subject | Registros médicos | spa |
| dc.subject | Artificial intelligence | eng |
| dc.subject | Medical records | eng |
| dc.title | Una introducción a las aplicaciones de la inteligencia artificial en Medicina: Aspectos históricos | spa |
| dc.title.alternative | An introduction to artificial intelligence applications in medicine: Historical aspects | eng |
| dc.type | article | eng |
| dc.type.driver | article | eng |
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| oaire.version | info:eu-repo/semantics/publishedVersion | spa |