A neural-network-based model for the removal of biomedical equipment from a hospital inventory

This article puts forward an accurate and robust model based on a artificial neural network that guarantees a warning when a piece of medical equipment requires replacement. A perceptron neural network composed by one input layer with two neurons is described. The artificial neural network can classify data in groups. In this research three groups were classified. These groups depend on numerical values of service cost/acquisition cost and usage time/useful life time ratios. A supervised learning rule to train the artificial neural network was selected. The training process was carried out by collecting typical data from 200 high-performance as well as 100 low-performance devices from four hospitals under study. The network was tested by collecting data (998 high-performance as well as 765 low-performance devices) in 4 hospitals. In 100 % of the cases the artificial neural network classified the equipment in the expected groups. It can be concluded that the network had a great level of data discrimination and an excellent performance level.

• Fecha de publicación: 2006
• Institución: Universidad del Rosario
• Repositorio: Repositorio EdocUR - U. Rosario
• Idioma: eng
• OAI Identifier: oai:repository.urosario.edu.co:10336/27609
• Acceso en línea: https://repository.urosario.edu.co/handle/10336/27609
• Palabra clave: Artificial neural network; Groups
• License: Restringido (Acceso a grupos específicos)