Search Results for author: Neil Jethani

Found 5 papers, 2 papers with code

A dynamic risk score for early prediction of cardiogenic shock using machine learning

no code implementations22 Mar 2023 Yuxuan Hu, Albert Lui, Mark Goldstein, Mukund Sudarshan, Andrea Tinsay, Cindy Tsui, Samuel Maidman, John Medamana, Neil Jethani, Aahlad Puli, Vuthy Nguy, Yindalon Aphinyanaphongs, Nicholas Kiefer, Nathaniel Smilowitz, James Horowitz, Tania Ahuja, Glenn I Fishman, Judith Hochman, Stuart Katz, Samuel Bernard, Rajesh Ranganath

We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock.

New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography

no code implementations5 May 2022 Neil Jethani, Aahlad Puli, Hao Zhang, Leonid Garber, Lior Jankelson, Yindalon Aphinyanaphongs, Rajesh Ranganath

We found ECG-based assessment outperforms the ADA Risk test, achieving a higher area under the curve (0. 80 vs. 0. 68) and positive predictive value (13% vs. 9%) -- 2. 6 times the prevalence of diabetes in the cohort.

FastSHAP: Real-Time Shapley Value Estimation

4 code implementations ICLR 2022 Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath

Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations.

Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations

1 code implementation2 Mar 2021 Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath

While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate.

Interpretable Machine Learning

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