Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:


Individualized sepsis treatment using reinforcement learning

Reinforcement learning is applied to two large databases of electronic health records for patients admitted to an intensive care unit to identify individualized treatment strategies for correcting hypotension in sepsis.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Komorowski et al. use RL to develop and validate a best-practice algorithm for vasopressor versus fluid dosing.


  1. Self, W. H. et al. Ann. Emerg. Med. 72, 457–466 (2018).

    Article  Google Scholar 

  2. Jaehne, A. K. & Rivers, E. P. Crit. Care Med. 44, 2263–2269 (2016).

    Article  Google Scholar 

  3. Malbrain, M. L. N. G. et al. Ann. Intensive Care 8, 66 (2018).

    Article  Google Scholar 

  4. Bai, X. et al. Crit. Care 18, 532 (2014).

    Article  Google Scholar 

  5. Marik, P. & Bellomo, R. Br. J. Anaesth. 116, 339–349 (2016).

    Article  CAS  Google Scholar 

  6. Komorowski, M., Celi, L. A., Badawi, O. & Gordon, A. C. Nat. Med. (2018).

    Article  CAS  Google Scholar 

  7. Sutton, R. S. & Barto, A. G. Introduction to Reinforcement Learning 135 (MIT Press, Cambridge, MA, USA, 1998).

    Book  Google Scholar 

  8. Xu, Y., Xu, Y. & Saria, S. A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves. in Machine Learning for Healthcare Conference 282–300 (2016).

  9. Prasad, N. et al. A reinforcement learning approach to weaning of mechanical ventilation in intensive care units. Preprint at (2017).

  10. Nemati, S., Ghassemi, M. M. & Clifford, G. D., 2016, August. Optimal Medication Dosing from Suboptimal Clinical Examples: A Deep Reinforcement Learning Approach. in 2016 IEEE 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2978–2981 (2016).

  11. Schulam, P. & Saria, S. Discretizing logged interaction data biases learning for decision-making. Preprint at (2018).

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Suchi Saria.

Ethics declarations

Competing interests

S.S. has received research support from the American Heart Association (Dallas) and an honorarium from AbbVie (Chicago) and has an ownership interest in Patient Ping (Boston) and Bayesian Health (Baltimore).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saria, S. Individualized sepsis treatment using reinforcement learning. Nat Med 24, 1641–1642 (2018).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing