ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges

no code implementations • 3 Dec 2023 • Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, Brandon Westover

In high-stakes medical applications, it is critical to have interpretable models so that experts can validate the reasoning of the model before making important diagnoses.

Decision Making EEG

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.

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