Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Angier Allen"'
Autor:
Abigail Green-Saxena, Jana Hoffman, Ritankar Das, Qingqing Mao, Angier Allen, Zohora Iqbal, Myrna Hurtado
Publikováno v:
BMJ Open Diabetes Research & Care, Vol 10, Iss 1 (2022)
Externí odkaz:
https://doaj.org/article/a1fd188814d4412b841de837e06b5934
Autor:
Sidney Le, Jana Hoffman, Christopher Barton, Julie C. Fitzgerald, Angier Allen, Emily Pellegrini, Jacob Calvert, Ritankar Das
Publikováno v:
Frontiers in Pediatrics, Vol 7 (2019)
Background: Early detection of pediatric severe sepsis is necessary in order to optimize effective treatment, and new methods are needed to facilitate this early detection.Objective: Can a machine-learning based prediction algorithm using electronic
Externí odkaz:
https://doaj.org/article/19b6715fe28c444ba96de112119459ac
Autor:
Angier Allen, Anna Siefkas, Emily Pellegrini, Hoyt Burdick, Gina Barnes, Jacob Calvert, Qingqing Mao, Ritankar Das
Publikováno v:
Applied Sciences, Vol 11, Iss 12, p 5576 (2021)
Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have pote
Externí odkaz:
https://doaj.org/article/2ddb76822b384f9e8266af3c6f174d41
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:
Debraj Basu, Jenish Maharjan, Angier Allen, Rahul Thapa, Misty M Attwood, Myrna Hurtado, Zohora Iqbal, Jana Hoffman
Background Despite the emergence of several promising machine learning models for prediction of patients at risk of sepsis, investigation of factors that contribute to false positive rates has not been performed. Here, we conducted a false positive a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e4a9b0d5f0814784e9cdf0f9ab4ae545
https://doi.org/10.21203/rs.3.rs-1746922/v1
https://doi.org/10.21203/rs.3.rs-1746922/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
Autor:
Logan Ryan, Abigail Green-Saxena, Emily Pellegrini, Carson Lam, Jana Hoffman, Angier Allen, Andrea McCoy, Samson Mataraso, Christopher Barton, Ritankar Das
Publikováno v:
Annals of Medicine and Surgery
Rationale Prediction of patients at risk for mortality can help triage patients and assist in resource allocation. Objectives Develop and evaluate a machine learning-based algorithm which accurately predicts mortality in COVID-19, pneumonia, and mech
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::584eec43aa51987b46e5077cf7f19849
https://doi.org/10.1016/j.amsu.2020.09.044
https://doi.org/10.1016/j.amsu.2020.09.044
Publikováno v:
Journal of Allergy and Clinical Immunology. 151:AB77
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4c81c516dbb803574dbba79dcc3a3453
https://doi.org/10.1016/j.jaci.2022.12.245
https://doi.org/10.1016/j.jaci.2022.12.245
Autor:
Anna Siefkas, Hoyt Burdick, Gina Barnes, Jacob Calvert, Ritankar Das, Emily Pellegrini, Qingqing Mao, Angier Allen
Publikováno v:
Applied Sciences
Applied Sciences, Vol 11, Iss 5576, p 5576 (2021)
Volume 11
Issue 12
Applied Sciences, Vol 11, Iss 5576, p 5576 (2021)
Volume 11
Issue 12
Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have pote
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e92a77a3be8ade72b616e2cd0fc9df98