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

Alexandre Kalimouttou; Romain Pirracchio

doi:10.1007/s00134-022-06972-y

Journal Articles Intensive Care Medicine Year : 2023

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

(1, 2) , (1, 3, 2)

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Alexandre Kalimouttou

Function : Author

Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité

Equipe 2 : ECSTRA - Epidémiologie Clinique, STatistique, pour la Recherche en Santé

Romain Pirracchio

Function : Author
PersonId : 1209393
ORCID : 0000-0002-7007-4908

Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité

Department of Anesthesia and Perioperative Medicine

Equipe 2 : ECSTRA - Epidémiologie Clinique, STatistique, pour la Recherche en Santé

Domains

Life Sciences [q-bio] Databases [cs.DB] Machine Learning [cs.LG] Infectious diseases

Ibrahima SOUANE : Connect in order to contact the contributor

https://hal-cnam.archives-ouvertes.fr/hal-04093499

Submitted on : Wednesday, May 10, 2023-11:16:53 AM

Last modification on : Friday, May 12, 2023-11:47:18 AM

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hal-04093499 , version 1 (10-05-2023)

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HAL Id : hal-04093499 , version 1
DOI : 10.1007/s00134-022-06972-y

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Alexandre Kalimouttou, Romain Pirracchio. Database-based machine learning in sepsis deserves attention. Author’s reply. Intensive Care Medicine, 2023, 49 (2), pp.264-265. ⟨10.1007/s00134-022-06972-y⟩. ⟨hal-04093499⟩

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

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