Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm.
Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm.
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DOI：
10.1038/s41746-022-00652-3
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发表时间：
2022-07-18
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
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作者：
Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.
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DOI：
10.1097/ta.0b013e31828c331d
发表时间：
2013-05
期刊：
The journal of trauma and acute care surgery
影响因子：
--
作者：
Haider AH;Weygandt PL;Bentley JM;Monn MF;Rehman KA;Zarzaur BL;Crandall ML;Cornwell EE;Cooper LA
通讯作者：
Cooper LA
影响因子：
38.9
作者：
Guiza, Fabian;Depreitere, Bart;Meyfroidt, Geert
通讯作者：
Meyfroidt, Geert
影响因子：
4.8
作者：
Carney, Nancy;Totten, Annette M.;Ghajar, Jamshid
通讯作者：
Ghajar, Jamshid
影响因子：
9.8
作者：
Pollard TJ;Johnson AEW;Raffa JD;Celi LA;Mark RG;Badawi O
通讯作者：
Badawi O
影响因子：
33.9
作者：
Daugherty, Jill;Waltzman, Dana;Xu, Likang
通讯作者：
Xu, Likang