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In complex settings, such as healthcare, predictive risk scores play an increasingly crucial role in guiding interventions. However, directly updating risk scores used to guide intervention can lead to biased risk estimates. To address this, we propo
Externí odkaz:
http://arxiv.org/abs/2202.06374
Autor:
Liley, James, Emerson, Samuel R, Mateen, Bilal A, Vallejos, Catalina A, Aslett, Louis J M, Vollmer, Sebastian J
Machine learning is increasingly being used to generate prediction models for use in a number of real-world settings, from credit risk assessment to clinical decision support. Recent discussions have highlighted potential problems in the updating of
Externí odkaz:
http://arxiv.org/abs/2010.11530
Akademický článek
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Predictive risk scores are increasingly used to guide clinical or other interventions in complex settings, particularly healthcare. Directly updating a risk score used to guide interventions leads to biased risk estimates. We propose updating using a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::889b1fdea90213f894818e35002f06c2
http://arxiv.org/abs/2202.06374
http://arxiv.org/abs/2202.06374
Autor:
Liley, James, Bohner, Gergo, Emerson, Samuel R., Mateen, Bilal A., Borland, Katie, Carr, David, Heald, Scott, Oduro, Samuel D., Ireland, Jill, Moffat, Keith, Porteous, Rachel, Riddell, Stephen, Rogers, Simon, Thoma, Ioanna, Cunningham, Nathan, Holmes, Chris, Payne, Katrina, Vollmer, Sebastian J., Vallejos, Catalina A., Aslett, Louis J. M.
Publikováno v:
NPJ Digital Medicine; 10/26/2024, Vol. 7 Issue 1, p1-1, 1p
