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- W4206719989 endingPage "675" @default.
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- W4206719989 abstract "Recovery after severe brain injury is variable and challenging to accurately predict at the individual patient level. This review highlights new developments in clinical prognostication with a special focus on the prediction of consciousness and increasing reliance on methods from data science.Recent research has leveraged serum biomarkers, quantitative electroencephalography, MRI, and physiological time-series to build models for recovery prediction. The analysis of high-resolution data and the integration of features from different modalities can be approached with efficient computational techniques.Advances in neurophysiology and neuroimaging, in combination with computational methods, represent a novel paradigm for prediction of consciousness and functional recovery after severe brain injury. Research is needed to produce reliable, patient-level predictions that could meaningfully impact clinical decision making." @default.
- W4206719989 created "2022-01-26" @default.
- W4206719989 creator A5000426332 @default.
- W4206719989 creator A5034691526 @default.
- W4206719989 creator A5049799218 @default.
- W4206719989 date "2020-10-20" @default.
- W4206719989 modified "2023-10-11" @default.
- W4206719989 title "Novel approaches to prediction in severe brain injury" @default.
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- W4206719989 doi "https://doi.org/10.1097/wco.0000000000000875" @default.
- W4206719989 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33105151" @default.
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