Matches in SemOpenAlex for { <https://semopenalex.org/work/W3033165612> ?p ?o ?g. }
- W3033165612 endingPage "102092" @default.
- W3033165612 startingPage "102075" @default.
- W3033165612 abstract "Recent developments in the field of machine learning (ML) have led to a renewed interest in the use of electroencephalography (EEG) to predict the outcome after traumatic brain injury (TBI). This systematic review aims to determine how previous studies have taken into consideration the important modeling issues for quantitative EEG (qEEG) predictors in developing prognostic models. A systematic search in the PubMed and Google Scholar databases was performed to identify all predictive models for the extended Glasgow outcome scale (GOSE) and Glasgow outcome scale (GOS) based on EEG data. Fourteen studies were identified that evaluated ML algorithms using qEEG predictors to predict outcome in patients with moderate to severe TBI. In each model, a maximum of five qEEG predictors were selected to determine the association between these parameters, and favorable or unfavorable predicted outcomes. The most common ML technique used was logistic regression, but the algorithms varied depending on the types and numbers of qEEG predictors selected in each model. The qEEG variability for the relative and absolute band powers were the most common qEEG predictors included in the models (46%) followed by total EEG power of all frequency bands (31%), EEG-reactivity (31%) and coherence (15%). Model performance was often quantified by the area under the receiving-operating characteristic curve (AUROC) rather than by accuracy rate. Various ML models have demonstrated great potential, especially using qEEG predictors, to predict outcome in patients with moderate to severe TBI." @default.
- W3033165612 created "2020-06-12" @default.
- W3033165612 creator A5000835810 @default.
- W3033165612 creator A5034307266 @default.
- W3033165612 date "2020-01-01" @default.
- W3033165612 modified "2023-09-27" @default.
- W3033165612 title "Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review" @default.
- W3033165612 cites W1566347475 @default.
- W3033165612 cites W1594364707 @default.
- W3033165612 cites W1860339752 @default.
- W3033165612 cites W1874700596 @default.
- W3033165612 cites W1967850515 @default.
- W3033165612 cites W1972724226 @default.
- W3033165612 cites W1993484478 @default.
- W3033165612 cites W1995875735 @default.
- W3033165612 cites W2005501262 @default.
- W3033165612 cites W2009217495 @default.
- W3033165612 cites W2019378382 @default.
- W3033165612 cites W2026177857 @default.
- W3033165612 cites W2026328515 @default.
- W3033165612 cites W2029492183 @default.
- W3033165612 cites W2041654382 @default.
- W3033165612 cites W2043235551 @default.
- W3033165612 cites W2048351707 @default.
- W3033165612 cites W2055068296 @default.
- W3033165612 cites W2059074157 @default.
- W3033165612 cites W2059624626 @default.
- W3033165612 cites W2077204677 @default.
- W3033165612 cites W2084236584 @default.
- W3033165612 cites W2087533499 @default.
- W3033165612 cites W2104441823 @default.
- W3033165612 cites W2108302150 @default.
- W3033165612 cites W2109325327 @default.
- W3033165612 cites W2115709314 @default.
- W3033165612 cites W2126436234 @default.
- W3033165612 cites W2131022184 @default.
- W3033165612 cites W2132029553 @default.
- W3033165612 cites W2148028185 @default.
- W3033165612 cites W2149881706 @default.
- W3033165612 cites W2150691612 @default.
- W3033165612 cites W2151254933 @default.
- W3033165612 cites W2151745375 @default.
- W3033165612 cites W2158700488 @default.
- W3033165612 cites W2158745924 @default.
- W3033165612 cites W2164214060 @default.
- W3033165612 cites W2165740328 @default.
- W3033165612 cites W2166072546 @default.
- W3033165612 cites W2171289936 @default.
- W3033165612 cites W2193627466 @default.
- W3033165612 cites W2230683965 @default.
- W3033165612 cites W2298802111 @default.
- W3033165612 cites W2301460319 @default.
- W3033165612 cites W2466421383 @default.
- W3033165612 cites W2520959200 @default.
- W3033165612 cites W2574580668 @default.
- W3033165612 cites W2588761628 @default.
- W3033165612 cites W2591294903 @default.
- W3033165612 cites W2626515687 @default.
- W3033165612 cites W2735428336 @default.
- W3033165612 cites W2747341096 @default.
- W3033165612 cites W2761529114 @default.
- W3033165612 cites W2762418274 @default.
- W3033165612 cites W2763556273 @default.
- W3033165612 cites W2768878216 @default.
- W3033165612 cites W2768899399 @default.
- W3033165612 cites W2790753862 @default.
- W3033165612 cites W2801117086 @default.
- W3033165612 cites W2802328741 @default.
- W3033165612 cites W2804407501 @default.
- W3033165612 cites W2885733125 @default.
- W3033165612 cites W2900530244 @default.
- W3033165612 cites W2900981882 @default.
- W3033165612 cites W2901140917 @default.
- W3033165612 cites W2911119486 @default.
- W3033165612 cites W2911964244 @default.
- W3033165612 cites W2939516305 @default.
- W3033165612 cites W2945186004 @default.
- W3033165612 cites W2965640442 @default.
- W3033165612 cites W2966977115 @default.
- W3033165612 cites W2993242472 @default.
- W3033165612 cites W2996323256 @default.
- W3033165612 cites W3105164764 @default.
- W3033165612 cites W4235120320 @default.
- W3033165612 cites W4293242440 @default.
- W3033165612 doi "https://doi.org/10.1109/access.2020.2998934" @default.
- W3033165612 hasPublicationYear "2020" @default.
- W3033165612 type Work @default.
- W3033165612 sameAs 3033165612 @default.
- W3033165612 citedByCount "16" @default.
- W3033165612 countsByYear W30331656122020 @default.
- W3033165612 countsByYear W30331656122021 @default.
- W3033165612 countsByYear W30331656122022 @default.
- W3033165612 countsByYear W30331656122023 @default.
- W3033165612 crossrefType "journal-article" @default.
- W3033165612 hasAuthorship W3033165612A5000835810 @default.
- W3033165612 hasAuthorship W3033165612A5034307266 @default.
- W3033165612 hasBestOaLocation W30331656121 @default.
- W3033165612 hasConcept C11413529 @default.