1 code implementation • 21 Jan 2023 • Chaoqi Yang, M. Brandon Westover, Jimeng Sun
Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e. g., 3. 7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning.
no code implementations • 9 Nov 2022 • Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Cynthia Rudin, M. Brandon Westover
Our interpretable model and specialized user interface can act as a reference for practitioners who work with cEEG patterns.
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%.
1 code implementation • 12 Jan 2022 • Tanish Tyagi, Colin G. Magdamo, Ayush Noori, Zhaozhi Li, Xiao Liu, Mayuresh Deodhar, Zhuoqiao Hong, Wendong Ge, Elissa M. Ye, Yi-han Sheu, Haitham Alabsi, Laura Brenner, Gregory K. Robbins, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Alberto Serrano-Pozo, Dimitry Prokopenko, Rudolph E. Tanzi, Bradley T. Hyman, Deborah Blacker, Shibani S. Mukerji, M. Brandon Westover, Sudeshna Das
Dementia related cognitive impairment (CI) is a neurodegenerative disorder, affecting over 55 million people worldwide and growing rapidly at the rate of one new case every 3 seconds.
1 code implementation • 13 Nov 2021 • Tanish Tyagi, Colin G. Magdamo, Ayush Noori, Zhaozhi Li, Xiao Liu, Mayuresh Deodhar, Zhuoqiao Hong, Wendong Ge, Elissa M. Ye, Yi-han Sheu, Haitham Alabsi, Laura Brenner, Gregory K. Robbins, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Alberto Serrano-Pozo, Dimitry Prokopenko, Rudolph E. Tanzi, Bradley T. Hyman, Deborah Blacker, Shibani S. Mukerji, M. Brandon Westover, Sudeshna Das
Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data.
1 code implementation • 27 Oct 2021 • Chaoqi Yang, Danica Xiao, M. Brandon Westover, Jimeng Sun
Objective: In this paper, we aim to learn robust vector representations from massive unlabeled EEG signals, such that the learned vectorized features (1) are expressive enough to replace the raw signals in the sleep staging task; and (2) provide better predictive performance than supervised models in scenarios of fewer labels and noisy samples.
no code implementations • 5 Mar 2021 • Zhen Lin, Cao Xiao, Lucas Glass, M. Brandon Westover, Jimeng Sun
Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting.
no code implementations • 24 Feb 2021 • Wolfgang Ganglberger, Abigail A. Bucklin, Ryan A. Tesh, Madalena Da Silva Cardoso, Haoqi Sun, Michael J. Leone, Luis Paixao, Ezhil Panneerselvam, Elissa M. Ye, B. Taylor Thompson, Oluwaseun Akeju, David Kuller, Robert J. Thomas, M. Brandon Westover
The objective is to automatically detect abnormal respiration and estimate the Apnea-Hypopnea-Index (AHI) with a wearable respiratory device, compared to an SpO2 signal or polysomnography using a large (n = 412) dataset serving as ground truth.
no code implementations • 26 Feb 2020 • Siddharth Biswal, Cao Xiao, Lucas M. Glass, M. Brandon Westover, Jimeng Sun
Most existing methods try to generate the whole reports from the raw input with limited success because 1) generated reports often contain errors that need manual review and correction, 2) it does not save time when doctors want to write additional information into the report, and 3) the generated reports are not customized based on individual doctors' preference.
no code implementations • 14 Oct 2019 • Irfan Al-Hussaini, Cao Xiao, M. Brandon Westover, Jimeng Sun
In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models.
Ranked #1 on Automatic Sleep Stage Classification on ISRUC-Sleep
no code implementations • 26 Mar 2018 • Olivier Deiss, Siddharth Biswal, Jing Jin, Haoqi Sun, M. Brandon Westover, Jimeng Sun
Although cEEG monitoring yields large volumes of data, labeling costs and difficulty make it hard to build a classifier.
no code implementations • 26 Jul 2017 • Siddharth Biswal, Joshua Kulas, Haoqi Sun, Balaji Goparaju, M. Brandon Westover, Matt T. Bianchi, Jimeng Sun
Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect 50-70 million adults in the United States (Hillman et al., 2006).