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The leap to ordinal: detailed functional prognosis after traumatic brain injury with a flexible modelling approach
We analysed the effect of 2 design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of 10 validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning.
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome Prediction
Combined EHR-PTS24 models had higher discrimination (area under the receiver operating characteristic curve [AUC]) than models which used either EHR or PTS24 alone, for the prediction of survival (AUC 0. 85, 0. 80 and 0. 68 respectively) and neurological outcome (0. 87, 0. 83 and 0. 78).
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