Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34161326&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215
Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values #MMPMID34161326
Cavallaro M; Moiz H; Keeling MJ; McCarthy ND
PLoS Comput Biol 2021[Jun]; 17 (6): e1009121 PMID34161326show ga
Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases.
free
free
free
PLoS+Comput+Biol 2021 ; 17 (6): e1009121
