Výsledky vyhledávání - "Hoyt Burdick"

Akademický článek

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

Autor: Hoyt Burdick, Eduardo Pino, Denise Gabel-Comeau, Carol Gu, Jonathan Roberts, Sidney Le, Joseph Slote, Nicholas Saber, Emily Pellegrini, Abigail Green-Saxena, Jana Hoffman, Ritankar Das

Publikováno v: BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-10 (2020)

Abstract Background Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of

Externí odkaz: https://doaj.org/article/8b37acccc4874a159454be0cf2d4bc26

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Akademický článek

Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

Autor: Jonathan Roberts, Andrea Mccoy, Hoyt Burdick, Eduardo Pino, Denise Gabel-Comeau, Carol Gu, Sidney Le, Joseph Slote, Emily Pellegrini, Abigail Green-Saxena, Jana Hoffman, Ritankar Das

Publikováno v: BMJ Health & Care Informatics, Vol 27, Iss 1 (2020)

Background Severe sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.Objecti

Externí odkaz: https://doaj.org/article/468470960701428b8e3a44aa970ec1f5

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Akademický článek

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

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

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Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids

Autor: Nicole S. Zelin, Jacob Calvert, Ritankar Das, Gina Barnes, Gregory Braden, Jana Hoffman, Carson Lam, Qingqing Mao, R. Phillip Dellinger, Hoyt Burdick, Jean Louis Vincent, Anna Siefkas

Publikováno v: Clinical therapeutics
Clinical Therapeutics

Purpose: Coronavirus disease–2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. Howev

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c717106e4b4dad4fadfb0226acc1573
https://doi.org/10.1016/j.clinthera.2021.03.016

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A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e92a77a3be8ade72b616e2cd0fc9df98

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Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial

Autor: Gina Barnes, Anna Siefkas, Hoyt Burdick, Gregory Braden, Abigail Green-Saxena, Jacob Calvert, Carson Lam, R. Phillip Dellinger, Ritankar Das, Jana Hoffman, Jean Louis Vincent, Emily Pellegrini, Andrea McCoy, Samson Mataraso

Publikováno v: Journal of Clinical Medicine
Volume 9
Issue 12
Journal of Clinical Medicine, Vol 9, Iss 3834, p 3834 (2020)

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f45e209778323299c6ba70c54ce5b133
http://europepmc.org/articles/PMC7760047

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Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial

Autor: Anna Siefkas, Abigail Green-Saxena, Carson Lam, Gina Barnes, Jana Hoffman, Gregory Braden, Jacob Calvert, R. Phillip Dellinger, Hoyt Burdick, Emily Pellegrini, Andrea McCoy, Jean Louis Vincent, Ritankar Das, Samson Mataraso

Publikováno v: Computers in biology and medicine, 124
Computers in Biology and Medicine

Background: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::626de2c628bca8c3a031df5163d5f32d
http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/312389

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A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study (Preprint)

Autor: Angier Allen, Samson Mataraso, Anna Siefkas, Hoyt Burdick, Gregory Braden, R Phillip Dellinger, Andrea McCoy, Emily Pellegrini, Jana Hoffman, Abigail Green-Saxena, Gina Barnes, Jacob Calvert, Ritankar Das

BACKGROUND Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities throug

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cfba7e2b2a8482e27b09b062b72a55a9
https://doi.org/10.2196/preprints.22400

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