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SUMMARY:Advancing Medicine through Data Science\, Machine Learning and Art
 ificial Intelligence  - Mihaela van der Schaar\, University of Oxford and 
 Alan Turing Institute
DTSTART:20170222T140000Z
DTEND:20170222T150000Z
UID:TALK69959@talks.cam.ac.uk
CONTACT:Rachel Furner
DESCRIPTION:In this talk\, I will describe some of the research of my grou
 p on developing and applying new machine learning methods for personalized
  healthcare\, with special focus on clinical decision support for chronic 
 conditions (including cancer\, cardio-vascular disease\, etc.) and for in-
 hospital care. Among other things\, I will discuss our development of data
 -driven methods for risk scoring and early warning systems\, for screening
  and diagnosis\, for prognosis and treatment.  Our novel machine learning 
 techniques take into account the unique characteristics of medical data an
 d embody a deep understanding of the medical domain\, achieved through con
 tinuous interaction with medical researchers and clinicians.  As a result\
 , our work achieves enormous improvements over current technology and over
  existing state-of-the-art machine learning methods.  Moreover\, our metho
 ds are designed to be easily interpretable by clinicians and to extract fr
 om data the necessary knowledge and representations to enable data-driven 
 medical epistemology and to allow easy adoption in hospitals and clinical 
 practice. You can find more information about our past research at: http:/
 /medianetlab.ee.ucla.edu/MedAdvance
LOCATION:MR15 Centre for Mathematical Sciences
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