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X-ORIGINAL-URL:https://canssiontario.utoronto.ca/
BEGIN:VEVENT
UID:MEC-d095e9553703cc6e3d058c3b70e8e6ce@canssiontario.utoronto.ca
DTSTART:20240429T193000Z
DTEND:20240429T203000Z
DTSTAMP:20230512T195400Z
CREATED:20230512
LAST-MODIFIED:20241205
SUMMARY:Statistical Sciences ARES: Larissa Stanberry
DESCRIPTION:\nJoin us at the Statistical Sciences Applied Research and Education Seminar (ARES) with\n\n\n\nLarissa Stanberry\n\n\n\nProgram DirectorData Science and Investigator Initiated ResearchMinneapolis Heart Institute Foundation\n\n\n\nFree Hybrid (In-person/Online) Event | Registration Required\n\n\n\nTalk Title\n\n\n\nClinical Prediction Models – Signal or Noise? A Case of Heart Failure\n\n\n\nAbstract\n\n\n\nMedical field is abuzz with artificial intelligence, that is disrupting health care by transforming its many aspects from image analysis and drug discoveries to patient monitoring, to healthcare operations and public health initiatives. In clinical research, this impact is felt through the steady increase in the number of clinical prediction models, proclaiming novel predictors and promising superior accuracy, intuitive use, and drastic improvements in patient outcomes and resource allocation. This increase is due not only to growing availability of healthcare data and developments in analysis methodology, but also, and in no small part, to advances in modern software lowering the barriers to entry. The democratization of technology and the advancement of user-friendly tools are allowing researchers with varying skill sets to try their luck in developing clinical prediction models.\n\n\n\nWe conducted a systematic review of research publications in PubMed 2018 – 2023 in the field of heart failure that were presented as developing clinical prediction models by their authors. The abstracted data elements were based on those identified in PROBAST (Prediction model Risk of Bias ASsessment Tool) and TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis). We evaluate the methodological conduct of the studies and present the sentiment analysis to estimate the prevalence of subjective or promotional language in the abstract corpus.\n\n\n\nSpeaker Profile\n\n\n\nDr. Stanberry is an experienced statistician and a program director with professional focus on bridging the gap between biomedical research and clinical practice. She completed her PhD in Statistics at the University of Washington in Seattle. Dr. Stanberry leads a cardiovascular research program at the Minneapolis Heart Institute Foundation, a non-profit research institute in Minneapolis, Minnesota. Her professional focus is on advancing clinical research through rigorous statistical treatment of data. She has authored and contributed to many scientific publications. Dr Stanberry also serves as a statistical editor of top tier research journals in cardiovascular field (JACC Heart Failure and JACC Advances) and NASA Human Research Program.\n
URL:https://canssiontario.utoronto.ca/event/ares-larissa-stanberry/
ORGANIZER;CN=CANSSI Ontario:MAILTO:esther.berzunza@utoronto.ca
CATEGORIES:Applied Research and Education Seminar​
LOCATION:Lumbers Building, 115 Ottawa Rd, North York, ON
ATTACH;FMTTYPE=image/png:https://canssiontario.utoronto.ca/wp-content/uploads/2023/05/Stanberry-Larissa.png
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