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Tytuł:
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SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest.
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Autorzy:
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Rao G; Department of Mathematics and Computing Science, Saint Mary's University, Halifax, CA, USA.
Choudhury S; Department of Computer Science, Lakehead University, Thunder Bay, CA, USA.
Lingras P; Department of Mathematics and Computing Science, Saint Mary's University, Halifax, CA, USA.
Savage D; Northern Ontario School of Medicine, Thunder Bay, CA, USA.
Mago V; Department of Computer Science, Lakehead University, Thunder Bay, CA, USA. .
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Źródło:
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BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2020 Dec 30; Vol. 20 (Suppl 11), pp. 313. Date of Electronic Publication: 2020 Dec 30.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Original Publication: London : BioMed Central, [2001-
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MeSH Terms:
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Emergency Medical Services*
Out-of-Hospital Cardiac Arrest*/diagnosis
Out-of-Hospital Cardiac Arrest*/therapy
Defibrillators ; Humans
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References:
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Circulation. 1997 Nov 18;96(10):3308-13. (PMID: 9396421)
Resuscitation. 2011 Aug;82(8):995-8. (PMID: 21570169)
Circulation. 2012 Jan 3;125(1):e2-e220. (PMID: 22179539)
Resuscitation. 2004 Oct;63(1):17-24. (PMID: 15451582)
Resuscitation. 2016 Jan;98:20-6. (PMID: 26475397)
Resuscitation. 2017 May;114:73-78. (PMID: 28268186)
Resuscitation. 2018 May;126:160-165. (PMID: 29408717)
N Engl J Med. 2008 Jan 3;358(1):9-17. (PMID: 18172170)
J Am Coll Cardiol. 2010 Apr 20;55(16):1713-20. (PMID: 20394876)
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Grant Information:
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IT14007 Mitacs; RGPIN-2017-05377 Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
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Contributed Indexing:
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Keywords: Integer linear programming (ILP); Internet of things (IoT); Optimization and allocation; Out-of-Hospital Cardiac Arrest (OHCA); Resource management
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Entry Date(s):
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Date Created: 20201231 Date Completed: 20210219 Latest Revision: 20210219
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Update Code:
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20240105
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PubMed Central ID:
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PMC7772910
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DOI:
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10.1186/s12911-020-01334-4
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PMID:
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33380330
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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.
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