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The most important factors in forecasting the primary result included lactate, age, total bilirubin, and creatinine. For the assessment cohort, the final design achieved 82% overall accuracy and a higher location underneath the curve (AUC) as compared to SAVE rating (0.92 vs 0.65, p=0.01) in predicting survival to discharge. Conclusions This evidence of idea study shows the potential for machine understanding models to augment clinical decision-making for VA-ECMO patients. Further developmen

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