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• W2952091391 abstract "Traumatic brain injury (TBI) is a leading cause of disability globally. Many patients develop post-traumatic epilepsy, or recurrent seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis that may assist in preventing seizure occurrence by identifying high-risk patients. We present a novel method of assessing seizure susceptibility using data from 49 patients enrolled in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We employ a machine learning paradigm that utilizes a Random Forest classifier trained with resting-state functional magnetic resonance imaging (fMRI) data to predict seizure outcomes. Following 100 rounds of stratified cross-validation with 70% of resting state fMRI scans as the training set and 30% as the testing set, our model was found to assess seizure outcome in the testing set with 69% accuracy. To validate the method, we compared our results with classification by Support Vector Machines and Neural Network classifiers." @default.
• W2952091391 created "2019-06-27" @default.
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• W2952091391 date "2019-04-01" @default.
• W2952091391 modified "2023-10-18" @default.
• W2952091391 title "A Machine Learning Model to Predict Seizure Susceptibility from Resting-State fMRI Connectivity" @default.
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• W2952091391 doi "https://doi.org/10.23919/springsim.2019.8732859" @default.
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