Individualized sepsis treatment using reinforcement learning

Suchi Saria^{1

2

3}

Affiliations

¹ Department of Computer Science and Applied Math and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.
² Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.
³ Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.

Comment

Suchi Saria. Nat Med. 2018 Nov.

. 2018 Nov;24(11):1641-1642.

doi: 10.1038/s41591-018-0253-x.

Suchi Saria^{1

2

3}

¹ Department of Computer Science and Applied Math and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.
² Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.
³ Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.

No abstract available

Comment on

The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care.
Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. Komorowski M, et al. Nat Med. 2018 Nov;24(11):1716-1720. doi: 10.1038/s41591-018-0213-5. Epub 2018 Oct 22. Nat Med. 2018. PMID: 30349085