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pro vyhledávání: '"Komorowski, M"'
Akademický článek
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Publikováno v:
In Procedia Engineering 2016 157:66-71
Autor:
Smit, J.M., Krijthe, J.H., van Bommel, Jasper, van Genderen, M. E., Labrecque, J. A., Komorowski, M., Gommers, D.A.M.P.J., Reinders, M.J.T.
Publikováno v:
Intensive Care Medicine
Artificial intelligence (AI) research in the intensive care unit (ICU) mainly focuses on developing models (from linear regression to deep learning) to predict out-comes, such as mortality or sepsis [1, 2]. However, there is another important aspect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::d40fb88a0a5d736c8d5d715ef5567293
http://resolver.tudelft.nl/uuid:38dd500a-7e71-4ae1-9b38-3d8c756c2637
http://resolver.tudelft.nl/uuid:38dd500a-7e71-4ae1-9b38-3d8c756c2637
Akademický článek
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Publikováno v:
ICML2021 workshop on Interpretable Machine Learning in Healthcare
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, automotive or aerospace, it is often challenging to bridge the gap betwee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8eb16799996a6d54347df6572cf7427c
http://arxiv.org/abs/2109.07827
http://arxiv.org/abs/2109.07827
Publikováno v:
Journal of Laryngology & Otology; Jan2023, Vol. 137 Issue 1, p7-16, 10p
Publikováno v:
COMPEL -The international journal for computation and mathematics in electrical and electronic engineering, 1998, Vol. 17, Issue 4, pp. 516-527.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/03321649810210802
Autor:
Patel, BV, Haar, S, Handslip, R, Lee, TM-L, Patel, S, Harston, JA, Hosking-Jervis, F, Kelly, D, Sanderson, B, Bogatta, B, Tatham, K, Welters, I, Camporota, L, Gordon, AC, Komorowski, M, Antcliffe, D, Prowle, JR, Puthucheary, Z, Faisal, AA
Background To date the description of mechanically ventilated patients with Coronavirus Disease 2019 (COVID-19) has focussed on admission characteristics with no consideration of the dynamic course of the disease. Here, we present a data-driven analy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1032::494c474e3a5b86749de04045af143dd8
http://hdl.handle.net/10044/1/84436
http://hdl.handle.net/10044/1/84436
Autor:
Nagendran, M, Chen, Y, Lovejoy, CA, Gordon, AC, Komorowski, M, Harvey, H, Topol, EJ, Ioannidis, JPA, Collins, GS, Maruthappu, M
Objective: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design: Systematic review.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1064::0bd63e363906e114996287fc26a81733
https://doi.org/10.1136/bmj.m689
https://doi.org/10.1136/bmj.m689
Autor:
Nagendran, M, Chen, Y, Lovejoy, C, Gordon, A, Komorowski, M, Harvey, H, Topol, E, Ioannidis, J, Collins, G, Maruthappu, M
Objective To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design Systematic review. Da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1032::177b5bb0e514c21866b02340578312b7
http://hdl.handle.net/10044/1/77933
http://hdl.handle.net/10044/1/77933