activity
20192022
most citedDeveloping Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks

15 citations · 29 across the 6 of their papers we have counts for

collaborators

9 papers

eess.SP2022

Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals

Jonathan Martinez, Kaan Sel, Bobak J. Mortazavi +1

Goal: To achieve-high quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morpholo…

cs.IR20203 cited

Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention

Justin Lovelace, Nathan C. Hurley, Adrian D. Haimovich +1

Problem lists are intended to provide clinicians with a relevant summary of patient medical issues and are embedded in many electronic health record systems. Despite their importan…

cs.LG202015 cited

Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks

Lida Zhang, Nathan C. Hurley, Bassem Ibrahim +4

Blood pressure monitoring is an essential component of hypertension management and in the prediction of associated comorbidities. Blood pressure is a dynamic vital sign with freque…

stat.ML2020

BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

Xiaochen Wang, Arash Pakbin, Bobak J. Mortazavi +2

The proliferation of medical monitoring devices makes it possible to track health vitals at high frequency, enabling the development of dynamic health risk scores that change with…

cs.LG20205 cited

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

Zepeng Huo, Arash PakBin, Xiaohan Chen +6

Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unk…

cs.LG20193 cited

Explainable Prediction of Adverse Outcomes Using Clinical Notes

Justin R. Lovelace, Nathan C. Hurley, Adrian D. Haimovich +1

Clinical notes contain a large amount of clinically valuable information that is ignored in many clinical decision support systems due to the difficulty that comes with mining that…