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<creators>
<creator>
<creatorName>Gaindric, C.V.</creatorName>
<affiliation>Institutul de Matematică şi Informatică "Vladimir Andrunachievici", Moldova, Republica</affiliation>
</creator>
<creator>
<creatorName>Cojocaru, S.C.</creatorName>
<affiliation>Institutul de Matematică şi Informatică "Vladimir Andrunachievici", Moldova, Republica</affiliation>
</creator>
<creator>
<creatorName>Pickl, S.</creatorName>
<affiliation>Bundeswehr University Munich, Germania</affiliation>
</creator>
<creator>
<creatorName>Nistor, M.</creatorName>
<affiliation>Bundeswehr University Munich, Germania</affiliation>
</creator>
<creator>
<creatorName>Secrieru, I.P.</creatorName>
<affiliation>Institutul de Matematică şi Informatică "Vladimir Andrunachievici", Moldova, Republica</affiliation>
</creator>
<creator>
<creatorName>Popcova, O.V.</creatorName>
<affiliation>Institutul de Matematică şi Informatică "Vladimir Andrunachievici", Moldova, Republica</affiliation>
</creator>
<creator>
<creatorName>Bein, D.</creatorName>
<affiliation>California State University, Fullerton, Statele Unite ale Americii</affiliation>
</creator>
<creator>
<creatorName>Cimpoeşu, D.</creatorName>
<affiliation>Universitatea de Medicină şi Farmacie „Gr.T. Popa“, Iaşi, România</affiliation>
</creator>
</creators>
<titles>
<title xml:lang='en'>A Concept for a Decision Support Framework for the Management of Complex Mass Casualty Situations at Distribution Points</title>
</titles>
<publisher>Instrumentul Bibliometric National</publisher>
<publicationYear>2018</publicationYear>
<relatedIdentifier relatedIdentifierType='ISBN' relationType='IsPartOf'>978‐9975‐4237‐7‐9</relatedIdentifier>
<subjects>
<subject>decision support</subject>
<subject>information technologies</subject>
<subject>distribution management</subject>
<subject>mass
casualty situations</subject>
<subject>on-site triage</subject>
<subject>medical ultrasound</subject>
<subject>reachback</subject>
</subjects>
<dates>
<date dateType='Issued'>2018</date>
</dates>
<resourceType resourceTypeGeneral='Text'>Conference Paper</resourceType>
<descriptions>
<description xml:lang='en' descriptionType='Abstract'><p>Very often disasters result in mass casualty situations, which trigger a complexity of decisions to be made at collection points and advanced medical posts by local health services. The decisionmaking process becomes complicated because of significant bleeding into the peritoneal, pleural, or pericardial spaces may occur without visible warning signs. We propose the design of an innovative decision support framework for the management of mass casualty situations at collection points via an artificial intelligence based multilayered approach, aimed to support decision-makers, dealing in a disaster area with a considerable number of casualties and have limited resources (ambulances, available nearby medical centers, and personnel).</p></description>
</descriptions>
<formats>
<format>application/pdf</format>
</formats>
</resource>