Cargando…

Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning

BACKGROUND AND OBJECTIVE: In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continu...

Descripción completa

Detalles Bibliográficos
Autores principales: Radhakrishnan, Sita, Nair, Suresh G., Isaac, Johney
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450520/
https://www.ncbi.nlm.nih.gov/pubmed/34567236
http://dx.doi.org/10.1016/j.bspc.2021.103170
_version_ 1784569665184333824
author Radhakrishnan, Sita
Nair, Suresh G.
Isaac, Johney
author_facet Radhakrishnan, Sita
Nair, Suresh G.
Isaac, Johney
author_sort Radhakrishnan, Sita
collection PubMed
description BACKGROUND AND OBJECTIVE: In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continuing despite the ever-increasing number of patients in critical epidemic conditions. Here a suggestive multi-layer perceptron neural network model is developed to predict the level of inspired oxygen delivered by the mechanical ventilator along with mode and positive end expiratory pressure (PEEP) changes for reducing the effort of health care professionals. METHODS: The artificial neural network model is developed by Python programming using real time data. Parameter identification for model inputs and outputs is done by in corporating consistent real time patient data including periodical arterial blood gas analysis, continuous pulse oximetry readings and mechanical ventilator settings using statistical pairwise analysis using R programming. RESULTS: Mean square error values and R values of the model are calculated and found to be an average of 0.093 and 0.81 respectively for various data sets. Accuracy loss will be in good fit with validation loss for a comparable number of epochs. CONCLUSIONS: Comparison of the model output is undertaken with physician’s prediction using statistical analysis and shows an accuracy error of 4.11 percentages which is permissible for a good predictive system.
format Online
Article
Text
id pubmed-8450520
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Elsevier Ltd.
record_format MEDLINE/PubMed
spelling pubmed-84505202021-09-20 Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning Radhakrishnan, Sita Nair, Suresh G. Isaac, Johney Biomed Signal Process Control Article BACKGROUND AND OBJECTIVE: In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continuing despite the ever-increasing number of patients in critical epidemic conditions. Here a suggestive multi-layer perceptron neural network model is developed to predict the level of inspired oxygen delivered by the mechanical ventilator along with mode and positive end expiratory pressure (PEEP) changes for reducing the effort of health care professionals. METHODS: The artificial neural network model is developed by Python programming using real time data. Parameter identification for model inputs and outputs is done by in corporating consistent real time patient data including periodical arterial blood gas analysis, continuous pulse oximetry readings and mechanical ventilator settings using statistical pairwise analysis using R programming. RESULTS: Mean square error values and R values of the model are calculated and found to be an average of 0.093 and 0.81 respectively for various data sets. Accuracy loss will be in good fit with validation loss for a comparable number of epochs. CONCLUSIONS: Comparison of the model output is undertaken with physician’s prediction using statistical analysis and shows an accuracy error of 4.11 percentages which is permissible for a good predictive system. Elsevier Ltd. 2022-01 2021-09-20 /pmc/articles/PMC8450520/ /pubmed/34567236 http://dx.doi.org/10.1016/j.bspc.2021.103170 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Radhakrishnan, Sita
Nair, Suresh G.
Isaac, Johney
Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning
title Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning
title_full Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning
title_fullStr Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning
title_full_unstemmed Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning
title_short Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning
title_sort multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450520/
https://www.ncbi.nlm.nih.gov/pubmed/34567236
http://dx.doi.org/10.1016/j.bspc.2021.103170
work_keys_str_mv AT radhakrishnansita multilayerperceptronneuralnetworkmodeldevelopmentformechanicalventilatorparameterspredictionbyrealtimesystemlearning
AT nairsureshg multilayerperceptronneuralnetworkmodeldevelopmentformechanicalventilatorparameterspredictionbyrealtimesystemlearning
AT isaacjohney multilayerperceptronneuralnetworkmodeldevelopmentformechanicalventilatorparameterspredictionbyrealtimesystemlearning