Abstract:
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.
journal_name
BMC Med Inform Decis Makjournal_title
BMC medical informatics and decision makingauthors
Rao G,Choudhury S,Lingras P,Savage D,Mago Vdoi
10.1186/s12911-020-01334-4subject
Has Abstractpub_date
2020-12-30 00:00:00pages
313issue
Suppl 11issn
1472-6947pii
10.1186/s12911-020-01334-4journal_volume
20pub_type
杂志文章abstract:BACKGROUND:Patients with no history of stroke but with stenosis of the carotid arteries can reduce the risk of future stroke with surgery or stenting. At present, a physicians' ability to recommend optimal treatments based on an individual's risk profile requires estimating the likelihood that a patient will have a poo...
journal_title:BMC medical informatics and decision making
pub_type: 杂志文章
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journal_title:BMC medical informatics and decision making
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journal_title:BMC medical informatics and decision making
pub_type: 杂志文章
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journal_title:BMC medical informatics and decision making
pub_type: 杂志文章,评审
doi:10.1186/s12911-015-0179-x
更新日期:2015-07-15 00:00:00
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journal_title:BMC medical informatics and decision making
pub_type: 杂志文章,meta分析
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journal_title:BMC medical informatics and decision making
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pub_type: 杂志文章,随机对照试验
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journal_title:BMC medical informatics and decision making
pub_type: 杂志文章
doi:10.1186/s12911-020-1053-z
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journal_title:BMC medical informatics and decision making
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journal_title:BMC medical informatics and decision making
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journal_title:BMC medical informatics and decision making
pub_type: 杂志文章
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更新日期:2019-03-18 00:00:00
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journal_title:BMC medical informatics and decision making
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更新日期:2006-01-06 00:00:00
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doi:10.1186/s12911-020-01365-x
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journal_title:BMC medical informatics and decision making
pub_type: 杂志文章,meta分析,评审
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更新日期:2014-06-20 00:00:00
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pub_type: 杂志文章
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更新日期:2017-07-11 00:00:00
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pub_type: 杂志文章
doi:10.1186/1472-6947-11-17
更新日期:2011-03-09 00:00:00
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journal_title:BMC medical informatics and decision making
pub_type: 杂志文章
doi:10.1186/1472-6947-3-3
更新日期:2003-02-13 00:00:00
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journal_title:BMC medical informatics and decision making
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journal_title:BMC medical informatics and decision making
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更新日期:2009-01-20 00:00:00
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pub_type: 杂志文章,评审
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