Zobrazeno 1 - 10
of 711
pro vyhledávání: '"P. Bourdeaux"'
Autor:
Clark, Jeffrey N., Wragg, Matthew, Nielsen, Emily, Perello-Nieto, Miquel, Keshtmand, Nawid, Ambler, Michael, Sharma, Shiv, Bourdeaux, Christopher P., Brigden, Amberly, Santos-Rodriguez, Raul
There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human-interpretable. This complexity raises concerns about trustw
Externí odkaz:
http://arxiv.org/abs/2411.11774
Intensive Care Units are complex, data-rich environments where critically ill patients are treated using variety of clinical equipment. The data collected using this equipment can be used clinical staff to gain insight into the condition of the patie
Externí odkaz:
http://arxiv.org/abs/2410.16959
Akademický článek
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Autor:
Bernard Formoso
Publikováno v:
Moussons, Vol 29, Pp 321-323 (2017)
Externí odkaz:
https://doaj.org/article/e99517f07d9a428ebb6cb7c77aa4f3cd
Intensive care units (ICUs) are complex and data-rich environments. Data routinely collected in the ICUs provides tremendous opportunities for machine learning, but their use comes with significant challenges. Complex problems may require additional
Externí odkaz:
http://arxiv.org/abs/2309.16500
Autor:
Clark, Jeffrey N., Small, Edward A., Keshtmand, Nawid, Wan, Michelle W. L., Mayoral, Elena Fillola, Werner, Enrico, Bourdeaux, Christopher P., Santos-Rodriguez, Raul
Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier. In this paper, we propose to extend the use of counterfactu
Externí odkaz:
http://arxiv.org/abs/2309.15965
Co-design is an effective method for designing software, but implementing it within the clinical setting comes with a set of unique challenges. This makes recruitment and engagement of participants difficult, which has been demonstrated in our study.
Externí odkaz:
http://arxiv.org/abs/2308.16631
Autor:
Werner, Enrico, Clark, Jeffrey N., Bhamber, Ranjeet S., Ambler, Michael, Bourdeaux, Christopher P., Hepburn, Alexander, McWilliams, Christopher J., Santos-Rodriguez, Raul
We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability t
Externí odkaz:
http://arxiv.org/abs/2301.08019
Akademický článek
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Autor:
Schröder, Fleur-Christine
Publikováno v:
Historisch-Politische Buch; January 2023, Vol. 71 Issue: 1-2 p79-80, 2p