Picture for Bobak J. Mortazavi

Bobak J. Mortazavi

ArterialNet: Reconstructing Arterial Blood Pressure Waveform with Wearable Pulsatile Signals, a Cohort-Aware Approach

Add code
Oct 24, 2024
Viaarxiv icon

An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data

Add code
Oct 11, 2024
Viaarxiv icon

Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

Add code
Jul 29, 2022
Figure 1 for Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Figure 2 for Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Figure 3 for Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Figure 4 for Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Viaarxiv icon

Predicting the meal macronutrient composition from continuous glucose monitors

Add code
Jun 23, 2022
Figure 1 for Predicting the meal macronutrient composition from continuous glucose monitors
Figure 2 for Predicting the meal macronutrient composition from continuous glucose monitors
Figure 3 for Predicting the meal macronutrient composition from continuous glucose monitors
Figure 4 for Predicting the meal macronutrient composition from continuous glucose monitors
Viaarxiv icon

Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals

Add code
Jan 11, 2022
Figure 1 for Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals
Figure 2 for Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals
Figure 3 for Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals
Figure 4 for Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals
Viaarxiv icon

Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards

Add code
Nov 10, 2021
Figure 1 for Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards
Figure 2 for Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards
Figure 3 for Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards
Figure 4 for Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards
Viaarxiv icon

BoXHED 2.0: Scalable boosting of functional data in survival analysis

Add code
Mar 23, 2021
Figure 1 for BoXHED 2.0: Scalable boosting of functional data in survival analysis
Figure 2 for BoXHED 2.0: Scalable boosting of functional data in survival analysis
Figure 3 for BoXHED 2.0: Scalable boosting of functional data in survival analysis
Figure 4 for BoXHED 2.0: Scalable boosting of functional data in survival analysis
Viaarxiv icon

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

Add code
Jul 25, 2020
Figure 1 for Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
Figure 2 for Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
Figure 3 for Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
Figure 4 for Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
Viaarxiv icon

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

Add code
Jul 24, 2020
Figure 1 for Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks
Figure 2 for Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks
Figure 3 for Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks
Figure 4 for Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks
Viaarxiv icon

BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

Add code
Jun 26, 2020
Figure 1 for BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Figure 2 for BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Figure 3 for BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Figure 4 for BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Viaarxiv icon