no code implementations • 23 Nov 2023 • Uddeshya Upadhyay, Sairam Bade, Arjun Puranik, Shahir Asfahan, Melwin Babu, Francisco Lopez-Jimenez, Samuel J. Asirvatham, Ashim Prasad, Ajit Rajasekharan, Samir Awasthi, Rakesh Barve
To address these challenges, we propose HypUC, a framework for imbalanced probabilistic regression in medical time series, making several contributions.
no code implementations • 17 Apr 2020 • FNU Shweta, Karthik Murugadoss, Samir Awasthi, AJ Venkatakrishnan, Arjun Puranik, Martin Kang, Brian W. Pickering, John C. O'Horo, Philippe R. Bauer, Raymund R. Razonable, Paschalis Vergidis, Zelalem Temesgen, Stacey Rizza, Maryam Mahmood, Walter R. Wilson, Douglas Challener, Praveen Anand, Matt Liebers, Zainab Doctor, Eli Silvert, Hugo Solomon, Tyler Wagner, Gregory J. Gores, Amy W. Williams, John Halamka, Venky Soundararajan, Andrew D. Badley
Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 15. 8 million clinical notes from 30, 494 patients subjected to COVID-19 PCR diagnostic testing.
no code implementations • 28 Mar 2020 • AJ Venkatakrishnan, Arjun Puranik, Akash Anand, David Zemmour, Xiang Yao, Xiaoying Wu, Ramakrishna Chilaka, Dariusz K. Murakowski, Kristopher Standish, Bharathwaj Raghunathan, Tyler Wagner, Enrique Garcia-Rivera, Hugo Solomon, Abhinav Garg, Rakesh Barve, Anuli Anyanwu-Ofili, Najat Khan, Venky Soundararajan
The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission.