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of 3
pro vyhledávání: '"Aaron E Boussina"'
Autor:
Parker Rogers, Aaron E Boussina, Supreeth P Shashikumar, Gabriel Wardi, Christopher A Longhurst, Shamim Nemati
Publikováno v:
Journal of Medical Internet Research, Vol 25, p e43486 (2023)
BackgroundSepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models. ObjectiveThe aim of this study was to optimize the parameters of a sepsis prediction model with
Externí odkaz:
https://doaj.org/article/a794160af61549aca05d58bdf61248aa
Autor:
Parker Rogers, Aaron E. Boussina, Supreeth P. Shashikumar, Gabriel Wardi, Christopher A. Longhurst, Shamim Nemati
BACKGROUND Recent advancements in machine learning (ML) and the proliferation of healthcare data have led to widespread excitement about using these technologies to improve care. Predictive analytic models in domains such as sepsis, acute kidney inju
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0eba737a23eb7746cb06b0c4e42f2751
https://doi.org/10.2196/preprints.43486
https://doi.org/10.2196/preprints.43486
Autor:
Parker Rogers, Aaron E. Boussina, Supreeth P. Shashikumar, Gabriel Wardi, Christopher A. Longhurst, Shamim Nemati
ObjectiveTo optimize the parameters of a sepsis prediction model within distinct patient groups to minimize the excess cost of sepsis care and analyze the potential effect of factors contributing to end-user response to sepsis alerts on overall model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a03200793f670d78b37f2983d2c42490
https://doi.org/10.1101/2022.08.28.22279313
https://doi.org/10.1101/2022.08.28.22279313