31 citations · 53 across the 8 of their papers we have counts for
8 papers
ArterialNet: Reconstructing Arterial Blood Pressure Waveform with Wearable Pulsatile Signals, a Cohort-Aware Approach
Sicong Huang, Roozbeh Jafari, Bobak J. Mortazavi
Goal: Continuous arterial blood pressure (ABP) waveform is invasive but essential for hemodynamic monitoring. Current non-invasive techniques reconstruct ABP waveforms with pulsati…
Realtime, multimodal invasive ventilation risk monitoring using language models and BoXHED
Arash Pakbin, Aaron Su, Donald K. K. Lee +1
Objective: realtime monitoring of invasive ventilation (iV) in intensive care units (ICUs) plays a crucial role in ensuring prompt interventions and better patient outcomes. Howeve…
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness
Zepeng Huo, Taowei Ji, Yifei Liang +4
In wearable sensing applications, data is inevitable to be irregularly sampled or partially missing, which pose challenges for any downstream application. An unique aspect of weara…
Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Zepeng Huo, Xiaoning Qian, Shuai Huang +2
Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive. Training models with this imbalance rate…
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis
Gregory Holste, Evangelos K. Oikonomou, Bobak J. Mortazavi +2
Advances in self-supervised learning (SSL) have shown that self-supervised pretraining on medical imaging data can provide a strong initialization for downstream supervised classif…
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
Randy Ardywibowo, Shahin Boluki, Zhangyang Wang +3
In many machine learning tasks, input features with varying degrees of predictive capability are acquired at varying costs. In order to optimize the performance-cost trade-off, one…
Growing Representation Learning
Ryan King, Bobak Mortazavi
Machine learning continues to grow in popularity due to its ability to learn increasingly complex tasks. However, for many supervised models, the shift in a data distribution or th…
Self-Damaging Contrastive Learning
Ziyu Jiang, Tianlong Chen, Bobak Mortazavi +1
The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in realit…