In this Comment, we provide guidelines for reinforcement learning for decisions about patient treatment that we hope will accelerate the rate at which observational cohorts can inform healthcare practice in a safe, risk-conscious manner.

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Author notes

  1. These authors contributed equally: Omer Gottesman, Fredrik Johansson.


  1. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    • Omer Gottesman
    •  & Finale Doshi-Velez
  2. Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA

    • Fredrik Johansson
    •  & David Sontag
  3. Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, MA, USA

    • Matthieu Komorowski
    •  & Leo Anthony Celi
  4. Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK

    • Matthieu Komorowski
  5. Department of Bioengineering, Imperial College London, London, UK

    • Aldo Faisal
  6. Department of Computing, Imperial College London, London, UK

    • Aldo Faisal
  7. Data Science Institute, London, UK

    • Aldo Faisal
  8. MRC London Institute of Clinical Sciences, London, UK

    • Aldo Faisal
  9. Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

    • Leo Anthony Celi
  10. MIT Critical Data, Cambridge, MA, USA

    • Leo Anthony Celi


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Competing interests

A.A.F. has received funding from Fresenius-KABI in the past.

Corresponding author

Correspondence to Leo Anthony Celi.

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