POSTCARDS from a SIESTA: Crossing the Translational and Generalizability Gap for Predictive Models of Acute Respiratory Distress Syndrome-Related Mortality
- PMID: 37971334
- PMCID: PMC10926350
- DOI: 10.1097/CCM.0000000000006061
POSTCARDS from a SIESTA: Crossing the Translational and Generalizability Gap for Predictive Models of Acute Respiratory Distress Syndrome-Related Mortality
Comment on
-
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 Dec 1;51(12):1638-1649. doi: 10.1097/CCM.0000000000006030. Epub 2023 Aug 31. Crit Care Med. 2023. PMID: 37651262
References
-
- Villar J, González-Martín JM, Hernández-González J, et al. Predicting ICU mortality in ARDS patients using machine learning: The POSTCARDS study. Crit Care Med 2023. In Press - PubMed
-
- 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 [Internet]. Annals of Translational Medicine 2021; 9[cited 2023 Aug 11] Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8246239/ - PMC - PubMed
-
- Holland JH: Genetic Algorithms [Internet]. Sci Am 1992; 267:66–73 Available from: http://www.jstor.org/stable/24939139
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Miscellaneous
