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Comment
. 2024 Feb 1;52(2):e106-e107.
doi: 10.1097/CCM.0000000000006115. Epub 2024 Jan 19.

The authors reply

Jesús Villar  1   2   3   4   5 , Jesús M González-Martin  2   3 , Tamas Szakmany  4   5
Affiliations
Comment

The authors reply

Jesús Villar et al. Crit Care Med. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

Dr. Szakmany received funding from ThermoFisher UK and PAION UK. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Comment on

References

    1. Valiente Fernández M, Lesmes González de Aledo A, Martin Badía I, et al.: Comparing Traditional Regression and Machine Learning Models in Predicting Acute Respiratory Distress Syndrome Mortality.. Crit Care Med. 2024; 52:e105–e106
    1. Villar J, González-Martín JM, Hernández-González J, et al.; Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Network: Predicting ICU mortality in acute respiratory distress syndrome patients using machine learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) study. Crit Care Med. 2023; 51:1638–1649
    1. Villar J, González-Martin JM, Ambrós A, et al.; Spanish Initiative for Epidemiology, Stratification and Therapies of ARDS (SIESTA) Network: Stratification for identification of prognostic categories in the acute respiratory distress syndrome (SPIRES) score. Crit Care Med. 2021; 49:e920–e930
    1. Bouvarel B, Carrat F, Lapidus N: Updating mortality risk estimation in intensive care units from high-dimensional electronic health records with incomplete data. BMC Med Inform Decis Mak. 2023; 23:170
    1. Kacmarek RM, Berra L: Prediction of ARDS outcome: What tool should I use? Lancet Respir Med. 2018; 6:253–254

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