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 0231031T170000
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 ing-clinical-risk-stratification-models-using-rank-based-compatibility
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility
As machine learning models become more integrated into clinical care, how can we update them without violating user expectations? We proposed a new rank-based compatibility measure and loss function to develop clinical AI that better aligns with physician mental models. High rank-based compatibility is not guaranteed but can be achieved through optimization, our approach yields updated models that better meet user expectations, promoting clinician-model team performance.\nPresenter:
 \nDr. Erkin 脰tle艧 is a distinguished Medical Scientist Training Program 
 Fellow at the University of Michigan, specializing in the nexus of AI and 
 medicine. With a robust background in health IT, particularly during his i
 mpactful tenure at Epic, he has significantly advanced AI integration into
  electronic medical records. Erkin鈥檚 dedication extends beyond research;
  he is actively shaping the future of healthcare with his work at the fore
 front of clinical informatics and AI-driven solutions. He holds a Master's
  of Engineering from the University of Wisconsin and aspires to further bl
 end clinical practice with technological innovation post his MD-PhD traini
 ng.\n\n\n\n      \n      \n\n\n\nDr. Erkin Otles Presentation\n\n\n\n
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