no code implementations • 19 Jan 2024 • Jodi Chiam, Aloysius Lim, Cheryl Nott, Nicholas Mark, Ankur Teredesai, Sunil Shinde
The ability to shape health behaviors of large populations automatically, across wearable types and disease conditions at scale has tremendous potential to improve global health outcomes.
no code implementations • 25 Apr 2023 • Tucker Stewart, Katherine Stern, Grant O'Keefe, Ankur Teredesai, Juhua Hu
Recently, deep learning methodologies have been proposed to predict sepsis, but some fail to capture the time of onset (e. g., classifying patients' entire visits as developing sepsis or not) and others are unrealistic for deployment in clinical settings (e. g., creating training instances using a fixed time to onset, where the time of onset needs to be known apriori).
no code implementations • 13 Apr 2023 • Kevin Ewig, Xiangwen Lin, Tucker Stewart, Katherine Stern, Grant O'Keefe, Ankur Teredesai, Juhua Hu
However, clinical scores like Sequential Organ Failure Assessment (SOFA) are not applicable for early prediction, while machine learning algorithms can help capture the progressing pattern for early prediction.
no code implementations • 15 Apr 2021 • Aloysius Lim, Ashish Singh, Jody Chiam, Carly Eckert, Vikas Kumar, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature.
no code implementations • 7 Feb 2021 • Ming Yuan, Vikas Kumar, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems.
no code implementations • 6 Feb 2021 • Karthik K. Padthe, Vikas Kumar, Carly M. Eckert, Nicholas M. Mark, Anam Zahid, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs).
no code implementations • 21 Sep 2020 • Boris Kovalerchuk, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Next, we present methods of visual discovery of ML models, with the focus on interpretable models, based on the recently introduced concept of General Line Coordinates (GLC).
no code implementations • 29 Jul 2019 • Muhammad Aurangzeb Ahmad, Carly Eckert, Ankur Teredesai
In this paper, we explore different settings where AI models with imputation can be problematic and describe ways to address such scenarios.
no code implementations • 10 Jun 2013 • Kiyana Zolfaghar, Nele Verbiest, Jayshree Agarwal, Naren Meadem, Si-Chi Chin, Senjuti Basu Roy, Ankur Teredesai, David Hazel, Paul Amoroso, Lester Reed
We first split the problem into various stages, (a) at risk in general (b) risk within 60 days (c) risk within 30 days, and then build suitable classifiers for each stage, thereby increasing the ability to accurately predict the risk using multiple layers of decision.