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Database-based machine learning in sepsis deserves attention. Author’s reply

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Availability of data and material

The datasets presented in the current study are available in the MIMIC-IV database (https://physionet.org/content/mimiciv/2.1/) and eICU database (https://physionet.org/content/eicu-crd/2.0/).

References

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Funding

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Author information

Authors and Affiliations

  1. Université Paris Cité and Université Sorbonne Paris Nord, Inserm UMR 1153, Center of Research in Epidemiology and Statistics (CRESS), ECSTRRA Team, 75004, Paris, France

    Alexandre Kalimouttou & Romain Pirracchio

  2. Department of Anesthesia and Perioperative Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California San Francisco, 1001 Potrero Avenue, San Francisco, CA, 94910, USA

    Romain Pirracchio

Authors
  1. Alexandre Kalimouttou
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  2. Romain Pirracchio
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Contributions

All the authors critically revised the manuscript and approved the final version.

Corresponding author

Correspondence to Romain Pirracchio.

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The authors declare that they have no competing interests.

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This comment refers to the article available online at https://doi.org/10.1007/s00134-022-06928-2.

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Kalimouttou, A., Pirracchio, R. Database-based machine learning in sepsis deserves attention. Author’s reply. Intensive Care Med 49, 264–265 (2023). https://doi.org/10.1007/s00134-022-06972-y

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  • DOI: https://doi.org/10.1007/s00134-022-06972-y

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