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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2021 2nd Sustainable Cities Latin America Conference, SCLA 2021
EditorsJorge R. Beingolea
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665437684
DOIs
StatePublished - 25 Aug 2021
Event2nd Sustainable Cities Latin America Conference, SCLA 2021 - Virtual, Online, Colombia
Duration: 25 Aug 202127 Aug 2021

Publication series

Name2021 2nd Sustainable Cities Latin America Conference, SCLA 2021

Conference

Conference2nd Sustainable Cities Latin America Conference, SCLA 2021
Country/TerritoryColombia
CityVirtual, Online
Period25/08/2127/08/21

Keywords

  • Machine Learning
  • Patient Monitor
  • Regression Analysis

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