Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm

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Raj , R , Wennervirta , J M , Tjerkaski , J , Luoto , T M , Posti , J P , Nelson , D W , Takala , R , Bendel , S , Thelin , E P , Luostarinen , T & Korja , M 2022 , ' Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm ' , npj digital medicine , vol. 5 , no. 1 , 96 . https://doi.org/10.1038/s41746-022-00652-3

Title: Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm
Author: Raj, Rahul; Wennervirta, Jenni M.; Tjerkaski, Jonathan; Luoto, Teemu M.; Posti, Jussi P.; Nelson, David W.; Takala, Riikka; Bendel, Stepani; Thelin, Eric P.; Luostarinen, Teemu; Korja, Miikka
Contributor organization: Helsinki University Hospital Area
Clinicum
HUS Neurocenter
Neurokirurgian yksikkö
HUS Perioperative, Intensive Care and Pain Medicine
Department of Diagnostics and Therapeutics
University of Helsinki
Anestesiologian yksikkö
Date: 2022-07-18
Language: eng
Number of pages: 8
Belongs to series: npj digital medicine
ISSN: 2398-6352
DOI: https://doi.org/10.1038/s41746-022-00652-3
URI: http://hdl.handle.net/10138/346607
Abstract: 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
Subject: UNITED-STATES
CARE
PRESSURE
OUTCOMES
3141 Health care science
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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