Herramienta para predecir el riesgo por exposición a radiaciones ionizantes en trabajadores del sector de la salud utilizando técnicas de Machine Learning

Exposure to ionizing radiation induces biological changes that can trigger high-cost or catastrophic diseases in those who handle them, these changes are not immediate, they can take time to appear and additionally these diseases have diverse factors, however, the quantification of ionizing radiation, hazard analysis and radiation weighting manage to categorize risks and prevent them from materializing. Health institutions must comply with scattered radiation measures that can be absorbed by workers while performing the medical act or in the environment where they perform their functions. These measurements are made with equipment such as dosimeters to have a monthly surveillance and to know the exposure values of the workers. This research uses the results of the measurement of the worker and the work environment and performs a risk categorization with the results obtained from health institutions. With the use of Machine Learning, the risk of absorbed and dispersed radiation in the work environment is weighted and with these values it is possible to design a tool that allows knowing and predicting the risk of exposure in which a worker is occupationally exposed.

• Autores: Rincón Arévalo, Guido Marcelo
• Tipo de recurso: Doctoral thesis
• Fecha de publicación: 2021
• Institución: Universidad Antonio Nariño
• Repositorio: Repositorio UAN
• Idioma: spa
• OAI Identifier: oai:repositorio.uan.edu.co:123456789/6616
• Acceso en línea: http://repositorio.uan.edu.co/handle/123456789/6616
• Palabra clave: Predicción; Riesgo; Exposición; Radiación; Ionizante; Salud; Aprendizaje automático; 600; Prediction; Risk; Radiation; Exposure; Ionizing; Health; Machine learning
• Rights: openAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)