More than half of patients who undergo targeted temperature management (TTM) after cardiac arrest do not survive hospitalization and 50% of those survivors suffer from long-term cognitive deficits. The goal of this study is to use machine learning methods to characterize the pattern of metabolic changes in patients with good and poor outcomes after cardiac arrest. A machine learning pipeline that incorporates z-scores, decision-tree modeling, principal component analysis, and linear support vector machine was applied to MR spectroscopy data acquired after cardiac arrest. Results confirm that N-acetylaspartate and lactate are important markers but other unexpected findings emerged as well.
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