Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Saurabh Johri"'
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and
Externí odkaz:
https://doaj.org/article/354f6c4524cb465f9ff459a9e2354e2a
Autor:
Yuanzhao Zhang, Robert Walecki, Joanne R. Winter, Felix J. S. Bragman, Sara Lourenco, Christopher Hart, Adam Baker, Yura Perov, Saurabh Johri
Publikováno v:
Frontiers in Digital Health, Vol 2 (2020)
Background: AI-driven digital health tools often rely on estimates of disease incidence or prevalence, but obtaining these estimates is costly and time-consuming. We explored the use of machine learning models that leverage contextual information abo
Externí odkaz:
https://doaj.org/article/5a40111c5bf440f5b050c37761d75fce
Autor:
Adam Baker, Yura Perov, Katherine Middleton, Janie Baxter, Daniel Mullarkey, Davinder Sangar, Mobasher Butt, Arnold DoRosario, Saurabh Johri
Publikováno v:
Frontiers in Artificial Intelligence, Vol 3 (2020)
AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a meaningful cont
Externí odkaz:
https://doaj.org/article/825ca61f0ed94061b1c92aad7079f6de
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-2 (2021)
A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21494-9
Externí odkaz:
https://doaj.org/article/8561d6759cee4aa68fdc084eedf592af
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-1 (2020)
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Externí odkaz:
https://doaj.org/article/63d07d2398f44d0184d2388128402f64
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-2 (2021)
A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21494-9
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bf39a409550a56f45973efe9b8208a3
https://doi.org/10.1038/s41467-021-21494-9
https://doi.org/10.1038/s41467-021-21494-9
Autor:
Robert Walecki, Christopher Hart, Sara Lourenco, Saurabh Johri, Adam Baker, Felix J. S. Bragman, Joanne R. Winter, Yuanzhao Zhang, Yura Perov
Publikováno v:
Frontiers in Digital Health, Vol 2 (2020)
Frontiers in Digital Health
Frontiers in Digital Health
Background: AI-driven digital health tools often rely on estimates of disease incidence or prevalence, but obtaining these estimates is costly and time-consuming. We explored the use of machine learning models that leverage contextual information abo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::993ee949f64f321c1db72fb573384f75
https://www.frontiersin.org/articles/10.3389/fdgth.2020.569261/full
https://www.frontiersin.org/articles/10.3389/fdgth.2020.569261/full
Autor:
Arnold DoRosario, Daniel Mullarkey, Yura Perov, Katherine Middleton, Davinder Sangar, Janie Baxter, M.Z. Butt, Saurabh Johri, Adam Baker
Publikováno v:
Frontiers in Artificial Intelligence
Frontiers in Artificial Intelligence, Vol 3 (2020)
Frontiers in Artificial Intelligence, Vol 3 (2020)
AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a meaningful cont
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::523b74a0c25d93255f8d696644cd595c
http://europepmc.org/articles/PMC7861270
http://europepmc.org/articles/PMC7861270
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b067d805408fca97f2e096812843a320
https://doi.org/10.1038/s41467-020-17419-7
https://doi.org/10.1038/s41467-020-17419-7
