23 citations · 47 across the 11 of their papers we have counts for
5 papers · 1 filter
Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects
Boyang Yu, Aakash Kaku, Kangning Liu +7
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel framework to address this challenge, which leverages AI model…
PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training
Avinash Parnandi, Aakash Kaku, Anita Venkatesan +7
Stroke rehabilitation seeks to increase neuroplasticity through the repeated practice of functional motions, but may have minimal impact on recovery because of insufficient repetit…
Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu +8
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization
Aakash Kaku, Sreyas Mohan, Avinash Parnandi +2
Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data. In this work, we show that the presence of suc…