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UID:20250722T012600EDT-2062NzUkdr@132.216.98.100
DTSTAMP:20250722T052600Z
DESCRIPTION:Jessica Gronsbell\, PhD\n\nAssistant Professor | Department of 
 Statistical Sciences |\n	University of Toronto\n\nWHEN: Wednesday\, Novembe
 r 29\, 2023\, from 3:30 to 4:30 p.m.\n\nWHERE: Hybrid | 2001 şÚÁϲ»´ňěČ Colleg
 e\, Rm 1140 | Zoom &\n\nNote: Dr. Gronsbell will present in-person\n\nAbst
 ract\n\nIn spite of the enormous increase in the volume and diversity of c
 linical data in the last decade\, the use of machine learning to improve p
 atient care remains a largely unfilled opportunity. A critical bottleneck 
 is the lack of methods that can properly address statistical inference que
 stions that arise in “life after machine learning”. Time permitting\, I wi
 ll consider two such questions. First\, I will show how to reliably evalua
 te a model’s performance and whether it is fair in the semi-supervised set
 ting when an extremely small proportion of testing data is labeled. Then\,
  I will discuss our recent method for regression modeling when the outcome
  of interest is predominantly derived from a machine learning model due to
  the time or expense of ascertainment. The practical utility of my proposa
 ls will be illustrated with analyses of electronic health record data from
  Mass General Brigham healthcare system and population biobank data from t
 he UK Biobank.\n\nSpeaker Bio\n\nJesse Gronsbell is an Assistant Professor
  in the Department of Statistical Sciences with cross-appointments in the 
 Departments of Family and Community Medicine and Computer Science at the U
 niversity of Toronto. She is interested in the development of statistical 
 learning and inference methods that address key challenges of analyzing mo
 dern observational health data\, including extreme missing data\, complex 
 measurement error\, data heterogeneity\, and bias and fairness. Jesse’s wo
 rk is primarily supported by NSERC\, CIHR\, and the Ontario Ministry of He
 alth. Website: https://sites.google.com/view/jgronsbell/home?authuser=0\n
DTSTART:20231129T203000Z
DTEND:20231129T213000Z
SUMMARY:Life after machine learning in health and medicine
URL:/epi-biostat-occh/channels/event/life-after-machin
 e-learning-health-and-medicine-352740
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