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Comment
. 2024 Feb 1;52(2):e105-e106.
doi: 10.1097/CCM.0000000000006084. Epub 2024 Jan 19.

Comparing Traditional Regression and Machine Learning Models in Predicting Acute Respiratory Distress Syndrome Mortality

Marcos Valiente Fernández  1 , Amanda Lesmes González de Aledo, Isaías Martín Badía, Francisco de Paula Delgado Moya
Affiliations
Comment

Comparing Traditional Regression and Machine Learning Models in Predicting Acute Respiratory Distress Syndrome Mortality

Marcos Valiente Fernández et al. Crit Care Med. .
No abstract available

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Conflict of interest statement

Dr. Delgado Moya disclosed government work. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Comment in

  • The authors reply.
    Villar J, González-Martin JM, Szakmany T. Villar J, et al. Crit Care Med. 2024 Feb 1;52(2):e106-e107. doi: 10.1097/CCM.0000000000006115. Epub 2024 Jan 19. Crit Care Med. 2024. PMID: 38240522 No abstract available.

Comment on

References

    1. Villar J, González-Martín JM, Hernández-González J, et al.: 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 Aug 30. [online ahead of print]
    1. Wolpert DH, Macready WG: No free lunch theorems for optimization. IEEE Trans Evol Comput. 1997; 1:67–82
    1. Huang B, Liang D, Zou R, et al.: Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: A population-based study. Ann Transl Med. 2021; 9:794
    1. Barboi C, Tzavelis A, Muhammad LN: Comparison of severity of illness scores and artificial intelligence models that are predictive of intensive care unit mortality: Meta-analysis and review of the literature. JMIR Med Inform. 2022; 10:e35293

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