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We present ICU-Sepsis, an environment that can be used in benchmarks for evaluating reinforcement learning (RL) algorithms.
Reinforcement learning in healthcare: A survey, 2020
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A quantitative description of membrane current and its application to conduction and excitation in nerve
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Dynamic Programming
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Model-free intelligent diabetes management using machine learning
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k-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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Modeling disease management decisions for patients with pneumonia-related sepsis
Jennifer E. Kreke · 2007
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Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach
Joelle Pineau, Arthur Guez, Robert Vincent, Gabriella Panuccio, and Massimo Avoli · 2009
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Markov decision processes
Johannes Fürnkranz, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, Jiawei Han, Ying Yang, Katharina Morik, Marco Dorigo, Mauro Birattari, Thomas Stützle, Pavel Brazdil, Ricardo Vilalta, Christophe Giraud-Carrier, Carlos Soares, Jorma Rissanen, Rohan A. Baxter, Ivan Bruha, Rohan A. Baxter, Geoffrey I. Webb, Luís Torgo, Arindam Banerjee, Hanhuai Shan, Soumya Ray, Prasad Tadepalli, Yoav Shoham, Rob Powers, Yoav Shoham, Rob Powers, Geoffrey I. Webb, Soumya Ray, Stephen Scott, Hendrik Blockeel, and Luc De Raedt · 2011
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Playing Atari with deep reinforcement learning, 2013
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Understanding vasoactive medications
John M. Allen · 2014
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Improving nurses' vasopressor titration skills and self-efficacy via simulation-based learning
Kristin Lavigne Fadale, Denise Tucker, Jennifer Dungan, and Valerie Sabol · 2014
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Sepsis guideline implementation: benefits, pitfalls and possible solutions
Niranjan Kissoon · 2014
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From data to optimal decision making: A data-driven, probabilistic machine learning approach to decision support for patients with sepsis
Athanasios Tsoukalas, Timothy Albertson, and Ilias Tagkopoulos · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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The third international consensus definitions for sepsis and septic shock (sepsis-3)
Mervyn Singer, Clifford S. Deutschman, Christopher Warren Seymour, Manu Shankar-Hari, Djillali Annane, Michael Bauer, Rinaldo Bellomo, Gordon R. Bernard, Jean-Daniel Chiche, Craig M. Coopersmith, Richard S. Hotchkiss, Mitchell M. Levy, John C. Marshall, Greg S. Martin, Steven M. Opal, Gordon D. Rubenfeld, Tom van der Poll, Jean-Louis Vincent, and Derek C. Angus · 2016
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Using reinforcement learning to personalize dosing strategies in a simulated cancer trial with high dimensional data
Kyle Humphrey · 2017
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Infectious diseases society of america (IDSA) position statement: Why IDSA did not endorse the Surviving Sepsis Campaign guidelines
Andre C Kalil, David N Gilbert, Dean L Winslow, Henry Masur, and Michael Klompas · 2017
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Reinforcement learning for sepsis treatment: Baselines and analysis, 2019
Aniruddh Raghu · 2019
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Identifying distinct, effective treatments for acute hypotension with SODA-RL: Safely optimized diverse accurate reinforcement learning, 2020
Joseph Futoma, Muhammad A. Masood, and Finale Doshi-Velez · 2020
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Safe reinforcement learning for sepsis treatment
Yan Jia, John Burden, Tom Lawton, and Ibrahim Habli · 2020
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Improving treatment decisions for sepsis patients by reinforcement learning
Ruishen Lyu · 2020
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Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the global burden of disease study
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Gizem Polat, Rustem Anil Ugan, Elif Cadirci, and Zekai Halici · 2017
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Deep reinforcement learning for sepsis treatment
Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo A. Celi, Peter Szolovits, and Marzyeh Ghassemi · 2017
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Proximal policy optimization algorithms, 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Electronic Health Record , pp. 1–6
Amnon Shabo · 2017
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Deep reinforcement learning for automated radiation adaptation in lung cancer
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On the identifiability of physiological models: Optimal design of clinical tests
Fabrizio Bezzo and Federico Galvanin · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, 2018
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care
Matthieu Komorowski, Leo A. Celi, Omar Badawi, Anthony C. Gordon, and A. Aldo Faisal · 2018
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Kristina E Rudd, Sarah Charlotte Johnson, Kareha M Agesa, Katya Anne Shackelford, Derrick Tsoi, Daniel Rhodes Kievlan, Danny V Colombara, Kevin S Ikuta, Niranjan Kissoon, Simon Finfer, Carolin Fleischmann-Struzek, Flavia R Machado, Konrad K Reinhart, Kathryn Rowan, Christopher W Seymour, R Scott Watson, T Eoin West, Fatima Marinho, Simon I Hay, Rafael Lozano, Alan D Lopez, Derek C Angus, Christopher J L Murray, and Mohsen Naghavi · 2020
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Sepsis cohort from MIMIC-III
Jayakumar Subramanian and Taylor Killian · 2020
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Surviving Sepsis Campaign: International guidelines for management of sepsis and septic shock 2021
Laura Evans, Andrew Rhodes, Waleed Alhazzani, Massimo Antonelli, Craig M. Coopersmith, Craig French, Flávia R. Machado, Lauralyn Mcintyre, Marlies Ostermann, Hallie C. Prescott, Christa Schorr, Steven Simpson, W. Joost Wiersinga, Fayez Alshamsi, Derek C. Angus, Yaseen Arabi, Luciano Azevedo, Richard Beale, Gregory Beilman, Emilie Belley-Cote, Lisa Burry, Maurizio Cecconi, John Centofanti, Angel Coz Yataco, Jan De Waele, R. Phillip Dellinger, Kent Doi, Bin Du, Elisa Estenssoro, Ricard Ferrer, Charles Gomersall, Carol Hodgson, Morten Hylander Møller, Theodore Iwashyna, Shevin Jacob, Ruth Kleinpell, Michael Klompas, Younsuck Koh, Anand Kumar, Arthur Kwizera, Suzana Lobo, Henry Masur, Steven McGloughlin, Sangeeta Mehta, Yatin Mehta, Mervyn Mer, Mark Nunnally, Simon Oczkowski, Tiffany Osborn, Elizabeth Papathanassoglou, Anders Perner, Michael Puskarich, Jason Roberts, William Schweickert, Maureen Seckel, Jonathan Sevransky, Charles L. Sprung, Tobias Welte, Janice Zimmerman, and Mitchell Levy · 2021
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CleanRL: High-quality single-file implementations of deep reinforcement learning algorithms
Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, and João G.M. Araújo · 2022
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Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment
Thesath Nanayakkara, Gilles Clermont, Christopher James Langmead, and David Swigon · 2022
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Optimizing the first response to sepsis: An electronic health record-based Markov decision process model
Erik Rosenstrom, Sareh Meshkinfam, Julie Simmons Ivy, Shadi Hassani Goodarzi, Muge Capan, Jeanne Huddleston, and Santiago Romero-Brufau · 2022
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Distributional Reinforcement Learning
Marc G. Bellemare, Will Dabney, and Mark Rowland · 2023
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MIMIC-III clinical database, 2023
Alistair Johnson, Tom Pollard, and Roger Mark · 2023
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Leveraging factored action spaces for efficient offline reinforcement learning in healthcare, 2023
Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, and Jenna Wiens · 2023
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Gymnasium, March 2023
Mark Towers, Jordan K. Terry, Ariel Kwiatkowski, John U. Balis, Gianluca de Cola, Tristan Deleu, Manuel Goulão, Andreas Kallinteris, Arjun KG, Markus Krimmel, Rodrigo Perez-Vicente, Andrea Pierré, Sander Schulhoff, Jun Jet Tai, Andrew Tan Jin Shen, and Omar G. Younis · 2023
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Towards more efficient and robust evaluation of sepsis treatment with deep reinforcement learning
Chao Yu and Qikai Huang · 2023
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