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SUMMARY:Revolutionizing Medicine through Machine Learning and Artificial I
 ntelligence  - Professor Mihaela van der Schaar\, University of Oxford and
  Alan Turing Institute
DTSTART:20170914T140000Z
DTEND:20170914T150000Z
UID:TALK80221@talks.cam.ac.uk
CONTACT:June Rix
DESCRIPTION:Current medical practice is driven by the experience of clinic
 ians\, by the difficulties of integrating enormous amounts of complex and 
 heterogeneous static and dynamic data and by clinical guidelines designed 
 for the “average” patient. In this talk\, I will describe some of my r
 esearch on developing novel\, specially-crafted machine learning theories\
 , methods and systems aimed at extracting actionable intelligence from the
  wide variety of information that is becoming available (in electronic hea
 lth records and elsewhere) and enabling every aspect of medical care to be
  personalized to the patient at hand. Because of the unique and complex ch
 aracteristics of medical data and medical questions\, many familiar machin
 e-learning methods are inadequate.  My work therefore develops and applies
  novel machine learning theory and methods to construct risk scores\, earl
 y warning systems and clinical decision support systems for screening and 
 diagnosis and for prognosis and treatment.  This work achieves enormous im
 provements over current clinical practice and over existing state-of-the-a
 rt machine learning methods.  By design\, these systems are easily interpr
 etable and so allow clinicians to extract from data the necessary knowledg
 e and representations to derive data-driven medical epistemology and to pe
 rmit easy adoption in hospitals and clinical practice. My team has collabo
 rated with researchers and clinicians in oncology\, emergency care\, cardi
 ology\, transplantation\, internal medicine\, etc. You can find more infor
 mation about our past research in this area at: http://medianetlab.ee.ucla
 .edu/MedAdvance.
LOCATION:MR3\,  Centre for Mathematical Sciences\, Wilberforce Road\, Camb
 ridge
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