NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage
Yi Li, Rebecca Anne Frederick, Daniel George, Stuart F. Cogan, Joseph Jewell Pancrazio, Leonidas G. Bleris, Ana Guadalupe Hernandez-Reynoso
This paper develops a Random Forest model to predict neurostimulation-induced tissue damage using multiple stimulation parameters, achieving 88.3% accuracy compared to 63.9% for the Shannon equation.
Journal of Neural Engineering · Jun 1, 2024Peer-reviewedNeuroscience