Interpretable Machine Learning System to EEG Patterns on the Ictal-Interictal-Injury Continuum

no code implementations • 9 Nov 2022 • Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Cynthia Rudin, M. Brandon Westover

To address these challenges, we propose a novel interpretable deep learning model that not only predicts the presence of harmful brainwave patterns but also provides high-quality case-based explanations of its decisions.

Electroencephalogram (EEG) Interpretable Machine Learning

Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients

no code implementations • 9 Mar 2022 • Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover

Having a maximum EA burden greater than 75% when untreated had a 22% increased chance of a poor outcome (severe disability or death), and mild but long-lasting EA increased the risk of a poor outcome by 14%.

Causal Inference Decision Making