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DESCRIPTION:SPECIAL SEMINAR\n\nRui (Ray) Fu\, PhD\n\nPostdoctoral Fellow in
  Evaluative Clinical Sciences\n	Department of Otolaryngology - Head & Neck 
 Surgery\n	Sunnybrook Health Sciences Centre & U of Toronto\n\nWHERE: In-Per
 son | 2001 ºÃÉ«TVl College\, Rm 1201 | Zoom\n\nAbstract\n\nDespite its grow
 ing popularity\, machine learning (ML) remains an unfamiliar concept for m
 any health researchers. In this presentation\, I will share my perspective
 s and experiences in learning and teaching ML\, with an emphasis on 1) rea
 listically situating ML in health research and 2) conducting and communica
 ting the analysis to different audiences. I will present 2 recently publis
 hed ML papers from my group. In the first paper\, we used survey data to c
 haracterize American high school students who were at risk of becoming add
 icted to electronic cigarettes (vaping)\, and in the second paper\, we app
 lied ML to linked administrative data to construct an algorithm for predic
 ting unplanned hospitalization and emergency department visits in head and
  neck cancer patients. I will end this presentation with cautionary notes 
 on how to report and interpret ML findings in a health manuscript and the 
 next phase in ML applications.\n\nSpeaker Bio\n\nRui Fu (Ray) is a Postdoc
 toral Fellow in Evaluative Clinical Sciences at the Department of Otolaryn
 gology – Head & Neck Surgery\, Sunnybrook Health Sciences Centre & Univers
 ity of Toronto. She is also affiliated with the Centre for Addiction and M
 ental Health where she works with trainees to use machine learning to stud
 y tobacco addiction. As a health services researcher\, Ray has a passion f
 or developing and creatively applying statistical methodology to analyze r
 eal-world data. Substantively\, she has delved into many fields of applica
 tion by being the primary statistician on the team. Her overarching goal i
 s to produce theory-driven and interpretable findings that can advance pol
 icymaking and quality of care\n\nRay’s ResearchGate:https://www.researchga
 te.net/profile/Rui-Fu-4\n
DTSTART:20230419T170000Z
DTEND:20230419T180000Z
SUMMARY:Machine learning for health researchers: beyond the FOMO (fear of m
 issing out)
URL:/epi-biostat-occh/channels/event/machine-learning-
 health-researchers-beyond-fomo-fear-missing-out-347637
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