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Abstract #3119

Machine learning–based prediction of post-concussive working memory decline: a 1-year fMRI prospective study

Yi-Tien Li1,2, Yung-Chieh Chen2, Yung-Li Chen3, Duen-Pang Kuo2, and Cheng-Yu Chen2
1Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan, 2Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan, 3Department of Occupational Therapy, Kaohsiung Medical University, Kaohsiung, Taiwan


The objective of this study is to construct a framework for precise individualized prediction of post-concussive cognitive outcomes based on the early fMRI and neuropsychological biomarkers assessed at baseline to facilitate early therapeutic intervention and individualized rehabilitation strategies. Satisfactory predictions can be achieved for patients whose WM function did not recover after 3 months (accuracy = 87.5%), 6 months (accuracy = 83.3%), and 1 year (accuracy = 83.3%). The results prove the feasibility of using machine learning–based approaches to reveal predictive biomarkers related to poor post-concussive cognitive outcomes.

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