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DTSTART:20070311T020000
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UID:42b9e831-bf23-4d79-a6e7-02ab4c5daa58.211035@calendar.missouristate.edu
CREATED:20201026T163925Z
LAST-MODIFIED:20201026T163925Z
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\nMassive amounts of data are being collected and analyzed using vari
 ous learning models with the objective of deriving useful discoveries tha
 t could transform or advance our society. Learning from the data collecte
 d is playing an increasingly important role in improving the quality of o
 ur healthcare. Machine learning (ML) can obtain insights into potential c
 ause and effect for diseases and other conditions related to healthcare. 
 This talk presents a framework for an explainable and statistically valid
 ated ensemble clustering model applied to Traumatic Brain Injury (TBI). T
 he objective of our analysis is to identify patient injury severity subgr
 oups and key phenotypes that delineate these subgroups using varied clini
 cal and computed tomography data. Explainable and statistically-validated
  models are essential because a data-driven identification of subgroups i
 s an inherently multidisciplinary undertaking. This framework for ensembl
 e cluster analysis fully integrates statistical methods at several stages
  of analysis to enhance the quality and the explainability of results.  \
 n\n\nDr. Tayo Obafemi-Ajayi is an Assistant Professor of Electrical Engin
 eering at Missouri State University (MSU) in the Cooperative Engineering 
 Program\, a joint program with Missouri S&amp;T. She is the director of the C
 omputational Learning Systems lab at MSU and the site coordinator of the 
 Missouri Louis Stokes Alliance for Minority Participation (MoLSAMP) progr
 am at MSU.\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;An
  Explainable and Statistically Validated Ensemble Clustering Model Applie
 d to the Identification of Traumatic Brain Injury Subgroups.&lt;/p&gt;\n&lt;p&gt;&lt;spa
 n&gt;Massive amounts of data are being collected and analyzed using various 
 learning models with the objective of deriving useful discoveries that co
 uld transform or advance our society. Learning from the data collected is
  playing an increasingly important role in improving the quality of our h
 ealthcare. Machine learning (ML) can obtain insights into potential cause
  and effect for diseases and other conditions related to healthcare. This
  talk presents a framework for an explainable and statistically validated
  ensemble clustering model applied to Traumatic Brain Injury (TBI). The o
 bjective of our analysis is to identify patient injury severity subgroups
  and key phenotypes that delineate these subgroups using varied clinical 
 and computed tomography data. Explainable and statistically-validated mod
 els are essential because a data-driven identification of subgroups is an
  inherently multidisciplinary undertaking. This framework for ensemble cl
 uster analysis fully integrates statistical methods at several stages of 
 analysis to enhance the quality and the explainability of results.&amp;nbsp\;
 &amp;nbsp\;&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span&gt;Dr. Tayo Obafemi-Ajayi is an Assistant Profe
 ssor of Electrical Engineering at Missouri State University (MSU) in the 
 Cooperative Engineering Program\, a joint program with Missouri S&amp;amp\;T.
  She is the director of the Computational Learning Systems lab at MSU and
  the site coordinator of the Missouri Louis Stokes Alliance for Minority 
 Participation (MoLSAMP) program at MSU.&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span&gt;Zoom M&lt;/span
 &gt;eeting ID: 969 7429 9371&lt;/p&gt;\n&lt;p&gt;Passcode: 519665&lt;/p&gt;\n&lt;p&gt;&lt;span&gt;&lt;/span&gt;&lt;
 /p&gt;&lt;/body&gt;&lt;/html&gt;
DTSTART;TZID=America/Chicago:20201030T111500
DTEND;TZID=America/Chicago:20201030T121500
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CATEGORIES:Current Students,Faculty,Staff
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