8 citations · 17 across the 7 of their papers we have counts for
7 papers
Validation of a Hospital Digital Twin with Machine Learning
Muhammad Aurangzeb Ahmad, Vijay Chickarmane, Farinaz Sabz Ali Pour +2
Recently there has been a surge of interest in developing Digital Twins of process flows in healthcare to better understand bottlenecks and areas of improvement. A key challenge is…
Machine Learning for Deferral of Care Prediction
Muhammad Aurangzeb Ahmad, Raafia Ahmed, Steve Overman +3
Care deferral is the phenomenon where patients defer or are unable to receive healthcare services, such as seeing doctors, medications or planned surgery. Care deferral can be the…
Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management
Aloysius Lim, Ashish Singh, Jody Chiam +4
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 mana…
Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare
Ming Yuan, Vikas Kumar, Muhammad Aurangzeb Ahmad +1
Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems. While the need for accountability of AI models is near ubiquitous,…
Emergency Department Optimization and Load Prediction in Hospitals
Karthik K. Padthe, Vikas Kumar, Carly M. Eckert +4
Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs). ED resources, including nurse staffing, are straine…
Survey of explainable machine learning with visual and granular methods beyond quasi-explanations
Boris Kovalerchuk, Muhammad Aurangzeb Ahmad, Ankur Teredesai
This paper surveys visual methods of explainability of Machine Learning (ML) with focus on moving from quasi-explanations that dominate in ML to domain-specific explanation support…
The Challenge of Imputation in Explainable Artificial Intelligence Models
Muhammad Aurangzeb Ahmad, Carly Eckert, Ankur Teredesai
Explainable models in Artificial Intelligence are often employed to ensure transparency and accountability of AI systems. The fidelity of the explanations are dependent upon the al…