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  <event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/188/Real-Time-Identification-of-Operating-Room-State-from-Video">
    <rdfs:label><![CDATA[Real-Time Identification of Operating Room State from Video]]></rdfs:label>
    <event:title><![CDATA[Real-Time Identification of Operating Room State from Video]]></event:title>
    <event:speaker><person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Beenish/Bhatia/"><person:name><![CDATA[Beenish  Bhatia]]></person:name><rdfs:label><![CDATA[Beenish  Bhatia]]></rdfs:label></person:Collaborator></event:speaker>
    <event:startDate rdf:datatype="&xsd;dateTime">2006-11-20T13:00:00-05:00</event:startDate>
    <event:endDate rdf:datatype="&xsd;dateTime">2006-11-20T02:30:00-05:00</event:endDate>
    <event:location><![CDATA[325b ITE]]></event:location>
    <event:abstract><![CDATA[Managers of operating rooms (ORs) and of units upstream and downstream of
the OR (e.g., postanesthesia care) seek real-time information about OR
occupancy to make decisions about managing OR workflow and coordinating
resources.  Nursing and anesthesia staff typically record patient in/out
times by hand, and OR managers spend time walking about the OR suite to
estimate the time each case will finish.  This thesis describes a system
for using real-time video to automatically identify the state of an ongoing
operation.  This state information is relevant to determining OR occupancy
and estimating time to case completion. The system, which uses support
vector machines to learn to identify image features relevant to state
identification and hidden Markov models to capture the sequential nature of
the domain, was tested on video captured over a two day period in one of
the nineteen ORs in Baltimore's R. Adams Cowley Shock Trauma Center.  An
overall accuracy of 99.5% was obtained for identifying for each video frame
the corresponding operation state, which was one of the following: OR
ready, patient entering, operation in progress, operation ending, patient
exiting.  Our results are contrasted with the current state-of-the-art
system used by the Shock Trauma Center which is based on patient vital
signs data.]]></event:abstract>
    <event:tag><![CDATA[medical informatics]]></event:tag>
    <event:tag><![CDATA[learning]]></event:tag>
    <event:host><person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Tim/Oates/"><person:name><![CDATA[Tim  Oates]]></person:name><rdfs:label><![CDATA[Tim  Oates]]></rdfs:label></person:Collaborator></event:host>
  </event:Event>

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