Development of a predictor software to determine the periodicity of patient monitors preventive maintenance in the intensive care unit of a health care institution

Juan Pablo Vargas Silva, Carlos Julian Lopez Salas, Mario Fernando Morales Cordero, Lusvin Javier Amado Forero, Carlos Julio Arizmendi Pereira

Producción científica: Libro / Capitulo del libro / InformeLibros de Investigaciónrevisión exhaustiva

Resumen

This project seeks to develop a software predictor to determine the amount of annual maintenance that a patient monitor (PM) will have in order to adjust the periodicity of preventive maintenance for such equipment. To achieve this, a database is created with which the predictor system is trained using regression methodologies such as: multinomial logistic regression, linear regression and stepwise regression. Principal component analysis (PCA) and feature selection by neighborhood component analysis for regression (FSRNCA) are implemented as dimensionality reduction methods. For software coding, Matlab and Python programming languages are used, and the stepwise regression methodology is implemented as a predictor model together with the PCA dimensionality reduction method, obtaining a success rate of approximately 65% with a standard deviation of approximately ±24.

Idioma originalInglés
Título de la publicación alojada2021 2nd Sustainable Cities Latin America Conference, SCLA 2021
EditoresJorge R. Beingolea
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665437684
DOI
EstadoPublicada - 25 ago. 2021
Evento2nd Sustainable Cities Latin America Conference, SCLA 2021 - Virtual, Online, Colombia
Duración: 25 ago. 202127 ago. 2021

Serie de la publicación

Nombre2021 2nd Sustainable Cities Latin America Conference, SCLA 2021

Conferencia

Conferencia2nd Sustainable Cities Latin America Conference, SCLA 2021
País/TerritorioColombia
CiudadVirtual, Online
Período25/08/2127/08/21

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