Please use this identifier to cite or link to this item:
https://repository.unad.edu.co/handle/10596/65143Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Galeano Diaz, Christian Eduardo | |
| dc.coverage.spatial | cead_-_pasto | |
| dc.creator | Muñoz Fernández, Leydi Yanixa | |
| dc.creator | Lasso Guastar, Jenifer Astrid | |
| dc.creator | Inca Ortiz, Karen Sofia | |
| dc.creator | Cuaran Cuaran, Yurany Estefanía | |
| dc.creator | Guerrero Pantoja, Marly Nathalya | |
| dc.date.accessioned | 2024-12-11T20:43:32Z | |
| dc.date.available | 2024-12-11T20:43:32Z | |
| dc.date.created | 2024-12-10 | |
| dc.identifier.uri | https://repository.unad.edu.co/handle/10596/65143 | |
| dc.description.abstract | Resumen El uso de inteligencia artificial (IA) para mejorar la seguridad y eficacia de medicamentos biológicos y biosimilares. Este enfoque combina tecnologías como el procesamiento de lenguaje natural, análisis predictivo y aprendizaje automático para detectar y evaluar eventos adversos, predecir riesgos y optimizar el monitoreo. La IA permite analizar grandes volúmenes de datos provenientes de registros médicos, reportes de farmacovigilancia y literatura científica, agilizando la identificación de patrones y la toma de decisiones informadas. Este modelo es crucial en medicamentos biológicos y biosimilares debido a su complejidad molecular y potencial de reacciones inmunogénicas. | |
| dc.format | ||
| dc.title | Farmacovigilancia Inteligente: un enfoque basado en IA para garantizar la seguridad y eficacia de medicamentos biológicos y biosimilares | |
| dc.type | Diplomado de profundización para grado | |
| dc.subject.keywords | Biosimilares | |
| dc.subject.keywords | Farmacovigilancia | |
| dc.subject.keywords | Inteligencia artificial | |
| dc.subject.keywords | Seguridad | |
| dc.subject.keywords | Medicamentos biológicos | |
| dc.description.abstractenglish | Abstract The use of artificial intelligence (AI) to improve the safety and efficacy of biological and biosimilar medicines. This approach combines technologies such as natural language processing, predictive analytics, and machine learning to detect and assess adverse events, predict risks, and optimize monitoring. AI makes it possible to analyze large volumes of data from medical records, pharmacovigilance reports, and scientific literature, streamlining the identification of patterns and making informed decisions. This model is crucial in biological and biosimilar medicines due to their molecular complexity and potential for immunogenic reactions. | |
| dc.subject.category | Regencia en farmacia | |
| Appears in Collections: | Diplomado de Farmacovigilancia | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Jalassog.pdf | 693.89 kB | Adobe PDF |  View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.