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Respiratory decision support systems
Computerized decision support systems help both health professionals and patients make the right diagnostic and therapeutic decisions in a timely fashion, capitalizing on the available knowledge as well as on data-driven methods. The wealth of data, which have been made available via connected healt...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245527/ http://dx.doi.org/10.1016/B978-0-12-823447-1.00008-7 |
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author | Chouvarda, Ioanna Perantoni, Eleni Steiropoulos, Paschalis |
author_facet | Chouvarda, Ioanna Perantoni, Eleni Steiropoulos, Paschalis |
author_sort | Chouvarda, Ioanna |
collection | PubMed |
description | Computerized decision support systems help both health professionals and patients make the right diagnostic and therapeutic decisions in a timely fashion, capitalizing on the available knowledge as well as on data-driven methods. The wealth of data, which have been made available via connected health technologies, the broader uptake of systems biology/medicine, and the technological progress of artificial intelligence are the three pillars leveraging such systems in respiratory care. In addition, for decision support, the following should be considered: (a) the complex interaction of the respiratory system with the circulatory and other systems, (b) the respiratory system decision-making spans in many directions and time scales, in acute and chronic care, and (c) the wealth of informative measurements and signals can contribute in this. This chapter will present methodological analysis for several respiratory disorders, namely sleep apnea, respiratory infections (including the COVID-19 pandemic), chronic lung diseases, and ventilation in respiratory care, including the data and methods applicable in each case. |
format | Online Article Text |
id | pubmed-9245527 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-92455272022-07-01 Respiratory decision support systems Chouvarda, Ioanna Perantoni, Eleni Steiropoulos, Paschalis Wearable Sensing and Intelligent Data Analysis for Respiratory Management Article Computerized decision support systems help both health professionals and patients make the right diagnostic and therapeutic decisions in a timely fashion, capitalizing on the available knowledge as well as on data-driven methods. The wealth of data, which have been made available via connected health technologies, the broader uptake of systems biology/medicine, and the technological progress of artificial intelligence are the three pillars leveraging such systems in respiratory care. In addition, for decision support, the following should be considered: (a) the complex interaction of the respiratory system with the circulatory and other systems, (b) the respiratory system decision-making spans in many directions and time scales, in acute and chronic care, and (c) the wealth of informative measurements and signals can contribute in this. This chapter will present methodological analysis for several respiratory disorders, namely sleep apnea, respiratory infections (including the COVID-19 pandemic), chronic lung diseases, and ventilation in respiratory care, including the data and methods applicable in each case. 2022 2022-06-17 /pmc/articles/PMC9245527/ http://dx.doi.org/10.1016/B978-0-12-823447-1.00008-7 Text en Copyright © 2022 Elsevier Inc. 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 Chouvarda, Ioanna Perantoni, Eleni Steiropoulos, Paschalis Respiratory decision support systems |
title | Respiratory decision support systems |
title_full | Respiratory decision support systems |
title_fullStr | Respiratory decision support systems |
title_full_unstemmed | Respiratory decision support systems |
title_short | Respiratory decision support systems |
title_sort | respiratory decision support systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245527/ http://dx.doi.org/10.1016/B978-0-12-823447-1.00008-7 |
work_keys_str_mv | AT chouvardaioanna respiratorydecisionsupportsystems AT perantonieleni respiratorydecisionsupportsystems AT steiropoulospaschalis respiratorydecisionsupportsystems |