BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Risky sex data: precision medicine\, big data and the ossification
  of a sex binary - Marion Boulicault (University of Edinburgh)
DTSTART:20240222T153000Z
DTEND:20240222T170000Z
UID:TALK211921@talks.cam.ac.uk
CONTACT:Lewis Bremner
DESCRIPTION:We are\, some say\, at the threshold of a medical revolution. 
 Current medical practice – which is based on a crude 'one size fits all'
  (or 'one size fits most') approach – will be replaced by 'precision med
 icine': an approach where big data and machine learning are harnessed to o
 ffer precisely tailored risk predictions\, diagnoses\, and treatment plans
  based on an individual's lifestyle\, environment\, and genetic make-up. I
 n this talk\, I look at the role of sex and gender data categories in the 
 development of precision medicine. I focus specifically on the case of pre
 cision medicine research on Alzheimer's\, dementia and related disorders\,
  a well-funded\, politically powerful\, and socially salient field of biom
 edicine with a history of contentious debate regarding the role of biologi
 cal and social factors in disease risk and prediction. I identify an assum
 ption that I call the 'default predictive value of sex' and show how this 
 assumption is fuelling calls for the development of sex-specific algorithm
 s and 'pink and blue' machine learning models. In doing so\, I show how th
 ese approaches to precision medicine risk naturalizing gender disparities 
 and ossifying a binary\, essentialized conception of sex in diagnostic and
  predictive tools.
LOCATION:Large Lecture Theatre\, Department of Plant Sciences\, Downing Si
 te
END:VEVENT
END:VCALENDAR