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DTSTART:20070311T020000
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DTSTART:20071104T020000
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UID:32cbb34b-250b-421a-bcc3-42d5fff03f12.210701@calendar.missouristate.edu
CREATED:20201005T190107Z
LAST-MODIFIED:20201005T190107Z
LOCATION:
SUMMARY:Computer Science Seminar - Dr. Tayo Obafemi-Ajayi
DESCRIPTION:An Explainable and Statistically Validated Ensemble Clustering
  Model Applied to the Identification of Traumatic Brain Injury Subgroups.
 \n\n\nAbstract:\n\n\nMassive amounts of data are being collected and anal
 yzed using various learning models with the objective of deriving useful 
 discoveries that could transform or advance our society. Learning from th
 e data collected is playing an increasingly important role in improving t
 he quality of our healthcare. Machine learning (ML) can obtain insights i
 nto potential cause and effect for diseases and other conditions related 
 to healthcare. This talk presents a framework for an explainable and stat
 istically validated ensemble clustering model applied to Traumatic Brain 
 Injury (TBI). The objective of our analysis is to identify patient injury
  severity subgroups and key phenotypes that delineate these subgroups usi
 ng varied clinical and computed tomography data. Explainable and statisti
 cally-validated models are essential because a data-driven identification
  of subgroups is an inherently multidisciplinary undertaking. This framew
 ork for ensemble cluster analysis fully integrates statistical methods at
  several stages of analysis to enhance the quality and the explainability
  of results.  This methodology is applicable to other clinical data sets 
 that exhibit significant heterogeneity as well as other diverse data scie
 nce applications in biomedicine and elsewhere.\n\n\nZoom link: https://mi
 ssouristate.zoom.us/j/96974299371?pwd=VjZieGxXdVRtUVJQeFREcDFpblU1QT09\n\
 n\nZoom Meeting ID: 969 7429 9371\n\n\nPasscode: 519665
X-ALT-DESC;FMTTYPE=text/html:&lt;html&gt;&lt;head&gt;&lt;title&gt;&lt;/title&gt;&lt;/head&gt;&lt;body&gt;&lt;p&gt;&lt;s
 trong&gt;&lt;span&gt;An Explainable and Statistically Validated Ensemble Clusterin
 g Model Applied to the Identification of Traumatic Brain Injury Subgroups
 .&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;b&gt;&lt;span&gt;&lt;span&gt;Abstract:&lt;/span&gt;&lt;/span&gt;&lt;/b&gt;&lt;/p&gt;\
 n&lt;p&gt;&lt;span&gt;Massive amounts of data are being collected and analyzed using 
 various learning models with the objective of deriving useful discoveries
  that could transform or advance our society. Learning from the data coll
 ected is playing an increasingly important role in improving the quality 
 of our healthcare. Machine learning (ML) can obtain insights into potenti
 al cause and effect for diseases and other conditions related to healthca
 re. This talk presents a framework for an explainable and statistically v
 alidated ensemble clustering model applied to Traumatic Brain Injury (TBI
 ). The objective of our analysis is to identify patient injury severity s
 ubgroups and key phenotypes that delineate these subgroups using varied c
 linical and computed tomography data. Explainable and statistically-valid
 ated models are essential because a data-driven identification of subgrou
 ps is an inherently multidisciplinary undertaking. This framework for ens
 emble cluster analysis fully integrates statistical methods at several st
 ages of analysis to enhance the quality and the explainability of results
 . &amp;nbsp\;This methodology is applicable to other clinical data sets that 
 exhibit significant heterogeneity as well as other diverse data science a
 pplications in biomedicine and elsewhere.&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span&gt;Zoom link:
 &amp;nbsp\;&lt;a href="https://missouristate.zoom.us/j/96974299371?pwd=VjZieGxXd
 VRtUVJQeFREcDFpblU1QT09"&gt;https://missouristate.zoom.us/j/96974299371?pwd=
 VjZieGxXdVRtUVJQeFREcDFpblU1QT09&lt;/a&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;Zoom Meeting ID: 969 
 7429 9371&lt;/p&gt;\n&lt;p&gt;&lt;span&gt;Passcode: 519665&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;/p&gt;\n&lt;p&gt;&lt;/p&gt;&lt;/bo
 dy&gt;&lt;/html&gt;
DTSTART;TZID=America/Chicago:20201023T111500
DTEND;TZID=America/Chicago:20201023T121500
SEQUENCE:0
URL:
CATEGORIES:Current Students,Faculty,Staff
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