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UID:c4f41991390116c307cea1e4a25a700f
CATEGORIES:Enterprise Imaging
SUMMARY:Probabilistic Terminology Management with a Machine Learning Model
LOCATION:Zoom Meeting
DESCRIPTION:<h5>Summary:</h5><p>The use of foreign exam management to share diagnostic 
 images (DI) and reports across disparate organizations has been well adopte
 d across various provinces and territories in Canada. It is common that eac
 h site contributing to a Diagnostic Imaging Repository (DIR) will have a un
 ique terminology lexicon local to that specific site. Deterministic termino
 logy mappings are often applied to associate a relationship between the loc
 al to regional DI procedure codes and names.</p><p>In this presentation, we
  will examine the use of Machine Learning to create a probabilistic model t
 o predict image type with 90% accuracy and offer a new unsupervised methodo
 logy that clusters the images based on similarities in their metadata.</p><
 h5>Learning objectives:</h5><ol><li>Identify the key challenges with semant
 ic interoperability as it relates to sharing exams between sites with dispa
 rate terminology lexicons.</li><li>Explain the process involved that built 
 the Machine Learning model to function successfully.</li><li>Understand how
  your organization can apply the use of this Machine Learning model and app
 reciate future opportunities this model may assist with.</li></ol><h5>Zoom 
 Meeting: <a href="https://zoom.us/j/3434442853">https://zoom.us/j/343444285
 3</a></h5><h5>Teleconference: 1-855-703-8985</h5><h5>Meeting code: 343 444 
 2853</h5>
CONTACT:Jason Nagels 
DTSTAMP:20260604T211255Z
DTSTART;TZID=America/New_York:20190920T120000
DTEND;TZID=America/New_York:20190920T130000
SEQUENCE:0
TRANSP:OPAQUE
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