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Abstract #2601

Using Machine Learning to Identify Metabolite Spectral Patterns that Reflect Outcome after Cardiac Arrest

Marcia Sahaya Louis1,2, Huijun Vicky Liao2, Rohit Singh3, Ajay Joshi1, Jong Woo Lee4, and Alexander Lin2
1ECE, Boston University, Boston, MA, United States, 2Radiology, Brigham and Women's Hospital, Boston, MA, United States, 3Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Brigham and Women's Hospital, Boston, MA, United States


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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