no code implementations • 22 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.
no code implementations • 27 Jan 2023 • Raghav Singhal, Mukund Sudarshan, Anish Mahishi, Sri Kaushik, Luke Ginocchio, Angela Tong, Hersh Chandarana, Daniel K. Sodickson, Rajesh Ranganath, Sumit Chopra
We hypothesise that the disease classification task can be solved using a very small tailored subset of k-space data, compared to image reconstruction.
no code implementations • 18 Aug 2022 • Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath
DIET tests the marginal independence of two random variables: $F(x \mid z)$ and $F(y \mid z)$ where $F(\cdot \mid z)$ is a conditional cumulative distribution function (CDF).
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.
1 code implementation • 2 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.
1 code implementation • NeurIPS 2020 • Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance.
no code implementations • 25 Sep 2019 • Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath
We show that f-divergences provide a broad class of proper test statistics.