IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-08816-7_46.html
   My bibliography  Save this book chapter

Prescient Profiling – AI Driven Volunteer Selection within a Volunteer Notification System

In: Automation, Communication and Cybernetics in Science and Engineering 2013/2014

Author

Listed:
  • Jesko Elsner

    (RWTH Aachen University, IMA/ZLW & IfU)

  • Philipp Meisen

    (RWTH Aachen University, IMA/ZLW & IfU)

  • Daniel Ewert

    (RWTH Aachen University, IMA/ZLW & IfU)

  • Daniel Schilberg

    (RWTH Aachen University, IMA/ZLW & IfU)

  • Sabina Jeschke

    (RWTH Aachen University, IMA/ZLW & IfU)

Abstract

A volunteer notification system (VNS) is a promising approach to integrate laypersons into emergency medical services (EMS). In case of a medical emergency, a VNS will alarm those potential helpers who can arrive on scene fast enough to provide the most urgent measures until the professional helpers arrive at the victim. Whereas the basic requirements and criteria of a VNS have been discussed in recent publications, this paper will focus on the actual volunteer selection process and the underlying concept of Prescient Profiling. By using concepts of artificial intelligence, the available data is processed in order to generate an abstract digital representation of a volunteer and further enhanced to produce individual user profiles. These profiles will enable predictions on future decisions and the identification of behavioral patterns within the pool of volunteers. The goal is to provide an efficient algorithm for determining a highly sophisticated set of relevant volunteers for an ongoing medical emergency.

Suggested Citation

  • Jesko Elsner & Philipp Meisen & Daniel Ewert & Daniel Schilberg & Sabina Jeschke, 2014. "Prescient Profiling – AI Driven Volunteer Selection within a Volunteer Notification System," Springer Books, in: Sabina Jeschke & Ingrid Isenhardt & Frank Hees & Klaus Henning (ed.), Automation, Communication and Cybernetics in Science and Engineering 2013/2014, edition 127, pages 597-607, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-08816-7_46
    DOI: 10.1007/978-3-319-08816-7_46
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-319-08816-7_46. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.