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UID:c4f41991390116c307cea1e4a25a700f
CATEGORIES:Enterprise Imaging
SUMMARY:Probabilistic Terminology Management with a Machine Learning Model
LOCATION:Zoom Meeting
DESCRIPTION:\n\nSummary:\n\nThe use of foreign exam management to share diagnostic imag
 es (DI) and reports across disparate organizations has been well adopted ac
 ross various provinces and territories in Canada. It is common that each si
 te contributing to a Diagnostic Imaging Repository (DIR) will have a unique
  terminology lexicon local to that specific site. Deterministic terminology
  mappings are often applied to associate a relationship between the local t
 o regional DI procedure codes and names.\n\n\nIn this presentation, we will
  examine the use of Machine Learning to create a probabilistic model to pre
 dict image type with 90% accuracy and offer a new unsupervised methodology 
 that clusters the images based on similarities in their metadata.\n\n\nLear
 ning objectives:\n - Identify the key challenges with semantic interoperabi
 lity as it relates to sharing exams between sites with disparate terminolog
 y lexicons.\n - Explain the process involved that built the Machine Learnin
 g model to function successfully.\n - Understand how your organization can 
 apply the use of this Machine Learning model and appreciate future opportun
 ities this model may assist with.\n\nZoom Meeting: https://zoom.us/j/343444
 2853\n\nTeleconference: 1-855-703-8985\n\nMeeting code: 343 444 2853
CONTACT:Jason Nagels 
DTSTAMP:20260604T210953Z
DTSTART;TZID=America/New_York:20190920T120000
DTEND;TZID=America/New_York:20190920T130000
SEQUENCE:0
TRANSP:OPAQUE
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