Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ryan Kindle"'
Autor:
Kristine Zhang, Henry Wang, Jianzhun Du, Brian Chu, Aldo Robles Arévalo, Ryan Kindle, Leo Anthony Celi, Finale Doshi-Velez
Publikováno v:
npj Digital Medicine, Vol 5, Iss 1, Pp 1-10 (2022)
Abstract Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clin
Externí odkaz:
https://doaj.org/article/fe27720701954c12a74797b3ec426440
Autor:
Arne Peine, Ahmed Hallawa, Johannes Bickenbach, Guido Dartmann, Lejla Begic Fazlic, Anke Schmeink, Gerd Ascheid, Christoph Thiemermann, Andreas Schuppert, Ryan Kindle, Leo Celi, Gernot Marx, Lukas Martin
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-12 (2021)
Abstract The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its perf
Externí odkaz:
https://doaj.org/article/cbf3e67d243d4b4c9450953527ffd35b
Autor:
Volker Roth, Sonali Parbhoo, Ryan Kindle, Maurizio Zazzi, Michael C. Hughes, Leo Anthony Celi, Mike Wu, Finale Doshi-Velez
Publikováno v:
AAAI
The lack of interpretability remains a barrier to adopting deep neural networks across many safety-critical domains. Tree regularization was recently proposed to encourage a deep neural network's decisions to resemble those of a globally compact, axi
Publikováno v:
Critical Care Medicine. 51:428-428
Autor:
Leo Anthony Celi, E. Mireles-Cabodevila, Marie-Laure Charpignon, An-Kwok Ian Wong, E. Monares-Zepeda, Ryan Kindle, L. Adhikari, R. W. M. A. Madushani, M. Kutner, H.B. Kim, Mary E. Lough, L. Carvalho
Publikováno v:
TP54. TP054 MECHANICAL VENTILATION.
Autor:
Kristine Zhang, Henry Wang, Jianzhun Du, Brian Chu, Aldo Robles Arévalo, Ryan Kindle, Leo Anthony Celi, Finale Doshi-Velez
Publikováno v:
NPJ digital medicine. 5(1)
Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to
Publikováno v:
Critical care clinics. 35(3)
This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities