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Database-based machine learning in sepsis deserves attention

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The Original Article was published on 29 November 2022

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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

  1. Kalimouttou A, Lerner I, Cheurfa C et al (2022) Machine-learning-derived sepsis bundle of care. Intensive Care Med. https://doi.org/10.1007/s00134-022-06928-2

    Article  PubMed  Google Scholar 

  2. Reyna MA, Josef CS, Jeter R et al (2020) Early prediction of sepsis from clinical data: the physionet/computing in cardiology challenge 2019. Crit Care Med 48(2):210–217

    Article  PubMed  PubMed Central  Google Scholar 

  3. Seymour CW, Gesten F, Prescott HC et al (2017) Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med 376(23):2235–2244

    Article  PubMed  PubMed Central  Google Scholar 

  4. Johnson A, Bulgarelli L, Pollard T, Horng S, Celi LA (2022) MIMIC‑IV (version 2.1). PhysioNet 2022. https://physionet.org/content/mimiciv/2.1/

  5. Pollard TJ, Johnson AEW, Raffa JD et al (2018) eICU Collaborative Research Database. https://physionet.org/content/eicu-crd/2.0/

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Funding

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Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao Road, Gulou District, Nanjing, 210009, People’s Republic of China

    Wenhan Hu

  2. The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, People’s Republic of China

    Meicheng Yang

  3. Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, Soochow University, No. 899 Pinghai Road, Suzhou, 215000, People’s Republic of China

    Hui Chen

Authors
  1. Wenhan Hu
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  2. Meicheng Yang
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  3. Hui Chen
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Contributions

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

Corresponding author

Correspondence to Hui Chen.

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The authors declare that they have no competing interests.

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Cite this article

Hu, W., Yang, M. & Chen, H. Database-based machine learning in sepsis deserves attention. Intensive Care Med 49, 262–263 (2023). https://doi.org/10.1007/s00134-022-06961-1

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  • DOI: https://doi.org/10.1007/s00134-022-06961-1

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