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SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest
BACKGROUND: When an Out-of-Hospital Cardiac Arrest (OHCA) incident is reported to emergency services, the 911 agent dispatches Emergency Medical Services to the location and activates responder network system (RNS), if the option is available. The RNS notifies all the registered users in the vicinit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772910/ https://www.ncbi.nlm.nih.gov/pubmed/33380330 http://dx.doi.org/10.1186/s12911-020-01334-4 |
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author | Rao, Gaurav Choudhury, Salimur Lingras, Pawan Savage, David Mago, Vijay |
author_facet | Rao, Gaurav Choudhury, Salimur Lingras, Pawan Savage, David Mago, Vijay |
author_sort | Rao, Gaurav |
collection | PubMed |
description | BACKGROUND: When an Out-of-Hospital Cardiac Arrest (OHCA) incident is reported to emergency services, the 911 agent dispatches Emergency Medical Services to the location and activates responder network system (RNS), if the option is available. The RNS notifies all the registered users in the vicinity of the cardiac arrest patient by sending alerts to their mobile devices, which contains the location of the emergency. The main objective of this research is to find the best match between the user who could support the OHCA patient. METHODS: For performing matching among the user and the AEDs, we used Bipartite Matching and Integer Linear Programming. However, these approaches take a longer processing time; therefore, a new method Preprocessed Integer Linear Programming is proposed that solves the problem faster than the other two techniques. RESULTS: The average processing time for the experimentation data was 1850 s using Bipartite matching, 32 s using the Integer Linear Programming and 2 s when using the Preprocessed Integer Linear Programming method. The proposed algorithm performs matching among users and AEDs faster than the existing matching algorithm and thus allowing it to be used in the real world. CONCLUSION: This research proposes an efficient algorithm that will allow matching of users with AED in real-time during cardiac emergency. Implementation of this system can help in reducing the time to resuscitate the patient. |
format | Online Article Text |
id | pubmed-7772910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77729102020-12-30 SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest Rao, Gaurav Choudhury, Salimur Lingras, Pawan Savage, David Mago, Vijay BMC Med Inform Decis Mak Research BACKGROUND: When an Out-of-Hospital Cardiac Arrest (OHCA) incident is reported to emergency services, the 911 agent dispatches Emergency Medical Services to the location and activates responder network system (RNS), if the option is available. The RNS notifies all the registered users in the vicinity of the cardiac arrest patient by sending alerts to their mobile devices, which contains the location of the emergency. The main objective of this research is to find the best match between the user who could support the OHCA patient. METHODS: For performing matching among the user and the AEDs, we used Bipartite Matching and Integer Linear Programming. However, these approaches take a longer processing time; therefore, a new method Preprocessed Integer Linear Programming is proposed that solves the problem faster than the other two techniques. RESULTS: The average processing time for the experimentation data was 1850 s using Bipartite matching, 32 s using the Integer Linear Programming and 2 s when using the Preprocessed Integer Linear Programming method. The proposed algorithm performs matching among users and AEDs faster than the existing matching algorithm and thus allowing it to be used in the real world. CONCLUSION: This research proposes an efficient algorithm that will allow matching of users with AED in real-time during cardiac emergency. Implementation of this system can help in reducing the time to resuscitate the patient. BioMed Central 2020-12-30 /pmc/articles/PMC7772910/ /pubmed/33380330 http://dx.doi.org/10.1186/s12911-020-01334-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Rao, Gaurav Choudhury, Salimur Lingras, Pawan Savage, David Mago, Vijay SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest |
title | SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest |
title_full | SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest |
title_fullStr | SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest |
title_full_unstemmed | SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest |
title_short | SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest |
title_sort | surf: identifying and allocating resources during out-of-hospital cardiac arrest |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772910/ https://www.ncbi.nlm.nih.gov/pubmed/33380330 http://dx.doi.org/10.1186/s12911-020-01334-4 |
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