7 citations · 15 across the 5 of their papers we have counts for
5 papers
A dynamic risk score for early prediction of cardiogenic shock using machine learning
Yuxuan Hu, Albert Lui, Mark Goldstein +18
Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US. The morbidity and mortality are highest among patients who devel…
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
Neil Jethani, Adriel Saporta, Rajesh Ranganath
Feature attribution methods identify which features of an input most influence a model's output. Most widely-used feature attribution methods (such as SHAP, LIME, and Grad-CAM) are…
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
Hao Zhang, Neil Jethani, Aahlad Puli +4
Diabetes has a long asymptomatic period which can often remain undiagnosed for multiple years. In this study, we trained a deep learning model to detect new-onset diabetes using 12…
FastSHAP: Real-Time Shapley Value Estimation
Neil Jethani, Mukund Sudarshan, Ian Covert +2
Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations. We introduce FastSHAP, a method for estima…
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs +1
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce th…