Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Cecilia Zeng"'
Autor:
Cecilia Zeng, Wang Xiang, Angier Allen, Sepideh Shokouhi, Satish Casie Chetty, Gina Barnes, Zohora Iqbal, Peiling Tsou, Navan Singh, Jacob Calvert, Myrna Hurtado, Jana Hoffman, Qingqing Mao
Importance: Despite sex and race disparities in the symptom presentation, diagnosis, and management of acute coronary syndrome (ACS), these differences have not been investigated in the development and validation of machine learning (ML) models using
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d825d4bcd3b42634a843b4b56e15634e
https://doi.org/10.21203/rs.3.rs-1743328/v1
https://doi.org/10.21203/rs.3.rs-1743328/v1
Autor:
Cecilia Zeng, Wang Xiang, Angier Allen, Sepideh Shokouhi, Satish Casie Chetty, Gina Barnes, Zohora Iqbal, Peiling Tsou, Jacob Calvert, Jana Hoffman, Qingqing Mao
Background Electronic health records (EHRs) contain individualized patient data that can be used to develop diagnostic and risk prediction models with artificial intelligence (AI) algorithms. Explicit and implicit sources of bias embedded in EHRs may
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8bd77238ac312c854a8e66fc85238c3
https://doi.org/10.21203/rs.3.rs-1735655/v1
https://doi.org/10.21203/rs.3.rs-1735655/v1
Autor:
Angier Allen, Cecilia Zeng, Chak Foon Tso, Navan Singh, Zohora Iqbal, Misty M Attwood, Veronica Gordon, Cindy Wang, Jana Hoffman
Background: Acute respiratory failure (ARF) presents within a spectrum of clinical manifestations and illness severity, and mortality occurs in approximately 30% of patients who develop ARF. Early risk identification is imperative for implementation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d56463e527883630fa7e071a15b071f
https://doi.org/10.21203/rs.3.rs-1668247/v1
https://doi.org/10.21203/rs.3.rs-1668247/v1