Publications

(For a full list of publications and patents see below or go to Google Scholar, GitHub)

Table of Contents

Group highlights

Multimodal Pretraining of Medical Time Series and Notes

Within the intensive care unit (ICU), a wealth of patient data, including clinical measurements and clinical notes, is readily available. This data is a valuable resource for comprehending patient health and informing medical decisions, but it also contains many challenges in analysis. Deep learning models show promise in extracting meaningful patterns, but they require extensive labeled data, a challenge in critical care. To address this, we propose a novel approach employing self-supervised pretraining, focusing on the alignment of clinical measurements and notes. Our approach combines contrastive and masked token prediction tasks during pretraining. Semi-supervised experiments on the MIMIC-III dataset demonstrate the effectiveness of our self-supervised pretraining. In downstream tasks, including in-hospital mortality prediction and phenotyping, our pretrained model outperforms baselines in settings where only a fraction of the data is labeled, emphasizing its ability to enhance ICU data analysis. Notably, our method excels in situations where very few labels are available, as evidenced by an increase in the AUC-ROC for in-hospital mortality by 0.17 and in AUC-PR for phenotyping by 0.1 when only 1% of labels are accessible. This work advances self-supervised learning in the healthcare domain, optimizing clinical insights from abundant yet challenging ICU data.

R. King, T. Yang, B. J. Mortazavi

Machine Learning for Health (ML4H) (2023)

BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis

Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from www.github.com/BoXHED.

A. Pakbin, X. Wang, B. J. Mortazavi, D. K. K. Lee

Journal of Statistical Software (JSS) (2023)

Variational Autoencoders for Biomedical Signal Morphology Clustering and Noise Detection

Accurate estimation of physiological biomarkers using raw waveform data from non-invasive wearable devices requires extensive data preprocessing. An automatic noise detection method in time-series data would offer significant utility for various domains. As data labeling is onerous, having a minimally supervised abnormality detection method for input data, as well as an estimation of the severity of the signal corruptness, is essential. We propose a model-free, time-series biomedical waveform noise detection framework using a Variational Autoencoder coupled with Gaussian Mixture Models, which can detect a range of waveform abnormalities without annotation, providing a confidence metric for each segment. Our technique operates on biomedical signals that exhibit periodicity of heart activities. This framework can be applied to any machine learning or deep learning model as an initial signal validator component. Moreover, the confidence score generated by the proposed framework can be incorporated into different models' optimization to construct confidence-aware modeling. We conduct experiments using dynamic time warping (DTW) distance of segments to validated cardiac cycle morphology. The result confirms that our approach removes noisy cardiac cycles and the remaining signals, classified as clean, exhibit a 59.92% reduction in the standard deviation of DTW distances. Using a dataset of bio-impedance data of 97885 cardiac cycles, we further demonstrate a significant improvement in the downstream task of cuffless blood pressure estimation, with an average reduction of 2.67 mmHg root mean square error (RMSE) of Diastolic Blood pressure and 2.13 mmHg RMSE of systolic blood pressure, with increases of average Pearson correlation of 0.28 and 0.08, with a statistically significant improvement of signal-to-noise ratio respectively in the presence of different synthetic noise sources. This enables burden-free validation of wearable sensor data for downstream biomedical applications.

Z. Nowroozilarki, B. J. Mortazavi, R. Jafari

IEEE Journal of Biomedical and Health Informatics (JBHI) (2023)

ArterialNet: Arterial Blood Pressure Reconstruction

Accurate and continuous monitoring of arterial blood pressure (ABP) is vital for clinical hemodynamic monitoring. However, current methods are either invasive, requiring insertion of catheters, or provide limited information, lacking comprehensive ABP waveforms. Cuffless wearable solutions, combined with deep learning, offer potential but face challenges in accurately reconstructing ABP waveforms and estimating systolic and diastolic blood pressure (SBP/DBP) due to individual variability. We propose a custom pre-trained backbone and a tailored optimization function to address these challenges. Our method demonstrates superior performance in ABP waveform reconstruction and accurate SBP/DBP estimations, while significantly reducing subject variance. To validate the effectiveness of our approach, we conducted comprehensive evaluations using both in-clinic data and a pioneering study involving remote health monitoring with cuffless data. Our results surpass previous efforts, demonstrating a root mean square error (RMSE) of 5.41 ± 1.35 mmHg and a minimum of 58% lower standard deviation (SD) across all measurements. These outcomes highlight the robustness and precision of our method in accurately estimating SBP/DBP and reconstructing ABP waveforms. Furthermore, we assessed the performance of our solution in non-clinical settings using the CTRAL BioZ dataset. The evaluation yielded an RMSE of 8.66 ± 1.13 mmHg for ABP, proving the potential of ABP reconstruction under remote health settings.

S. Huang, R. Jafari, B. J. Mortazavi

IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2023)

Oral Acceptance Rate 12%

 

Journal Papers

[J1] Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications
S. Huang, R. Jafari, B. J. Mortazavi
IEEE Open Journal of Engineering in Medicine and Biology (OJEMB) (2024)

[J2] BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis
A. Pakbin, X. Wang, B. J. Mortazavi, D. K. K. Lee
Journal of Statistical Software (JSS) (2023)

[J3] Variational Autoencoders for Biomedical Signal Morphology Clustering and Noise Detection
Z. Nowroozilarki, B. J. Mortazavi, R. Jafari
IEEE Journal of Biomedical and Health Informatics (JBHI) (2023)

[J4] Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation
N. C. Hurley, S. S. Dhruva, N. R. Desai, J. R. Ross, C. G. Ngufor, F. Masoudi, H. M. Krumholz, B. J. Mortazavi
ACM Transactions on Computing for Healthcare (HEALTH) (2023)

[J5] A Review of Digital Innovations for Diet Monitoring and Precision Nutrition
B. J. Mortazavi, R. Gutierrez-Osuna
Journal of Diabetes Science and Technology (2021)

[J6] A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders
N. C. Hurley, E. Spatz, H. Krumholz, R. Jafari, B. J. Mortazavi
ACM Transactions on Computing for Healthcare (2020)

[J7] Protocol for Project Recovery: Cardiac Surgery-Leveraging Digital Platform for Efficient Collection of Longitudinal Patient-Reported Outcome Data Towards Improving Postoperative Recovery
M. Mori, C. Brooks, E. Spatz, B. J. Mortazavi, S. S. Dhruva, G. Linderman, L. Grab, Y. Zhang, A. Geirsson, S. Chaudhry, H. Krumholz
BMJ Open (2020)

[J8] Intravascular Microaxial Left Ventricular Assist Device vs. Intra-aortic Balloon Pump for Cardiogenic Shock
S. S. Dhruva, B. J. Mortazavi, N. Desai
Journal of the American Medical Association (JAMA 2020)

[J9] Recommendations for Reporting Machine Analyses in Clinical Research
Stevens, L., B. J. Mortazavi, R. Deo, L. Curtis, D. Kao
Circulation Cardiovascular Quality and Outcomes (Circ: CQO 2020)

[J10] Using Intelligent Personal Annotations to Improve Human Activity Recognition for Movements in Natural Environments
A. Akbari, R. Solis, R. Jafari, B. J. Mortazavi
IEEE Journal of Biomedical and Health Informatics (J-BHI 2020)

[J11] Federal Funding for Clinical Research Applying Machine Learning Techniques in 2017: An Analysis of the NIH RePORTER
Annapureddy, A., Angraal, S., Caraballo-Cordovez, C., Grimshaw, A., Huang, C., B. J. Mortazavi, Krumholz, HM.
NPJ Digital Medicine (2017)

[J12] Understanding the Complex Relationship Between Contrast Volume During Percutaneous Coronary Intervention and the Risk of Acute Kidney Injury
Huang, C., Li, SX., Mahajan, S., Testani, J., Wilson, F., Mena, C., Masoudi, FM., Rumsfeld, J., Spertus, J., B. J. Mortazavi, Krumholz, HM.
JAMA Network Open. 2(11): e1916021-e1916021

[J13] Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
N. C. Hurley, A. Haimovich, Taylor, R., B. J. Mortazavi
Elsevier Smart Health. (Special Issue: Proceedings of the ACM/IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies

[J14] Predicting Mortality and Hospitalization in Patients with Heart Failure with Preserved Ejection Fraction
Angraal, S.+, B. J. Mortazavi+, Gupta, A., Khera, R., Ahmad, T., Desai, N., Jacoby, D., Masoudi, F., Spertus, J., H. Krumholz (+ equal contribution first authors)
JACC Heart Failure

[J15] Statistical Machine Learning Techniques to Enhance Risk Prediction of Bleeding After Percutaneous Coronary Intervention
B. J. Mortazavi, Bucholz, E., Desai, N., Huang, C., J. Curtis, Masoudi, F., Shaw, R., Negahban, S., Krumholz, H
JAMA Network Open

[J16] Phenotypes of hypertensive ambulatory blood pressure patterns: Design and rationale of the ECHORN Hypertension Study
E. Spatz, Martinez-Brockman, J., Tessier-Sherman, B., B. J. Mortazavi, Roy, B., Schwartz, J., Nazario, C., Maharaj, R., Nunez, M., Adams, O. P., Burg, M., Nunez-Smith, M
Ethnicity and Disease.

[J17] Enhancing the Prediction of Acute Kidney Injury Risk after Percutaneous Coronary Intervention using Machine Learning Techniques: A retrospective cohort study
Huang, C., Murugiah, K., Mahajan, S., Li, S-X., S. S. Dhruva, Haimovich, J., Wang, Y., Schulz, W., Testani, J., Wilson, F., Mena, C., Masoudi, F., Rumsfeld, J., Spertus, J., B. J. Mortazavi, H. Krumholz (* equal contribution senior author)
Plos Medicine 15 (11): e1002703.

[J18] A Survey on Smart Homes for Aging in Place: Toward Solutions to the Specific Needs of the Elderly
Nathan, V., Paul, S., Prioleau, T., Niu, Li, B. J. Mortazavi, Cambone, S., Veeraraghavan, A., Sabharwal, A., Jafari, R
IEEE Signal Processing Magazine 35 (5): 111-119

[J19] Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures
B. J. Mortazavi, Desai, N., Zhang, J., Coppi, A., Warner, F., H. Krumholz, Negahban, S.
IEEE Journal of Biomedical and Health Informatics (J-BHI) 21 (6), 1719-172

[J20] Probabilistic Segmentation of Time-Series Audio Signals using Support Vector Machines
Kalantarian, H., B. J. Mortazavi, Pourhomayoun, M., Alshurafa, N., M. Sarrafzadeh
Microprocessors and Microsystems. Vol. 46, pp: 96-104

[J21] Analysis of Machine Learning Techniques for Heart Failure Readmissions
B. J. Mortazavi, Downing, N., Bucholz, E., Dharmarajan, K., Manhapra, A., Li, S., Negahban, S., H. Krumholz
CIRCOUTCOMES, 116.003039

[J22] The Rickettsia Endosymbiont of Ixodes Pacificus Contains All Genes of De Novo Folate
Hunter, D., Torkelson, J., Bodnar, J., B. J. Mortazavi, Laurent, T., Deason, J., Thephavongsa, K., Zhong, J.
Biosynthesis. PLOS One 10 (12) e0144552

[J23] User-optimized Activity Recognition for Exergaming
B. J. Mortazavi, Pourhomayoun, M., Nyamathi, S., Wu, B., S. I. Lee, M. Sarrafzadeh
Elsevier Journal of Pervasive and Mobile Computing 26, 3-16

[J24] Objectively Quantifying Walking Ability in Degenerative Spinal Disorder Patients using Sensor Equipped Smart Shoes
S. I. Lee, E. Park, A. Huang, B. J. Mortazavi, J.H. Garst, N. Jahanforouz, M. Espinal , T. Siero, S. Pollack, M. Afridi, M. Daneshvar, S. Ghias, D.C. Lu, Sarrafzadeh, M
Medical Engineering & Physics (Med Eng Phys) 38 (5). 442-449

[J25] Quantitative Assessment of Hand Motor Function in Cervical Spinal Disorder Patients Using Target Tracking Tests
S. I. Lee, Li, C., Hoffman, H., Lu, D., Getachew, R., B. J. Mortazavi, Garst, J., Espinal, M., Razaghy, M., Ghalehsari, N., Paak, B., Chavam, A., Afridi, M., Ostowari, A., H. Ghasemzadeh, Lu, D., M. Sarrafzadeh
Journal of Rehabilitation Research and Development (J-RRD) 53 (6)

[J26] A Comparison of Piezoelectric-Based Inertial Sensing and Audio-Based Detection of Swallows
Kalantarian, H., B. J. Mortazavi, Alshurafa, N., Sideris, C., Le, T., M. Sarrafzadeh
Elsevier Journal of Obesity. 1, 6-14

[J27] Can Smartwatches Replace Smartphones for Posture Tracking?
B. J. Mortazavi, Nemati, E., VanderWall, K., Flores-Rodriguez, H., Cai, J., Lucier, J., Naeim, A., M. Sarrafzadeh
Sensors. Vol. 15, no. 10, pp. 26783-26800

[J28] Improving Biomedical Signal Search Results in Big Data Case-Based Reasoning Environments
J. Woodbridge, B. J. Mortazavi, Bui, A.A.T., M. Sarrafzadeh
Elsevier Journal of Pervasive and Mobile Computing 2016, vol 28, 69-80

[J29] A Prediction Model for Functional Outcomes in Spinal Cord Injured Patients Using Gaussian Process Regression
S. I. Lee, B. J. Mortazavi, Hoffman, H., Lu, D., Paak, B., Garst, J., Razaghy, M., Lu, D., M. Sarrafzadeh
IEEE Journal of Biomedical and Health Informatics (J-BHI) Vol 20, 1:91-99

[J30] Context-Aware Data Processing to Enhance Quality Measurements in Wireless Health Systems: An Application to MET Calculation of Exergaming Actions
B. J. Mortazavi, Pourhomayoun, M., H. Ghasemzadeh, R. Jafari, C. Roberts, M. Sarrafzadeh
IEEE Internet of Things Journal (J-IOT). Vol. 2, no. 1, pp. 84-93

[J31] Designing a Robust Activity Recognition Framework for Health and Exergaming using Wearable Sensors
Alshurafa, N., Xu, W., Liu, J., Huang, M.C., B. J. Mortazavi, C. Roberts, M. Sarrafzadeh
IEEE Journal of Biomedical and Health Informatics (J-BHI). Vol. 18, no. 5, pp. 1636-1646

[J32] Near-Realistic Mobile Exergames with Wireless Wearable Sensors
B. J. Mortazavi, Nyamathy, S., S. I. Lee, Wilkerson, T., H. Ghasemzadeh, M. Sarrafzadeh
IEEE Journal of Biomedical and Health Informatics (J-BHI). Vol. 18, no. 2, pp. 449-456. Featured Article - March, 2014

[J33] Pervasive Assessment of Motor Function: A Lightweight Grip Strength Tracking System
S. I. Lee, H. Ghasemzadeh, B. J. Mortazavi, Sarrafzadeh, M
IEEE Journal of Biomedical and Health Informatics (J-BHI). Vol. 17, no. 6, pp. 1023-1030

Conference Papers

[C1] Non-invasive Electrolyte Estimation Using Multi-lead ECG data via Semi-supervised Contrastive Learning with an Adaptive Loss
Z. Nowroozilarki, S. Huang, R. Khera, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2024)

[C2] A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability
R. King, C. Krueger, E. Veselka, T. Yang, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2024)

[C3] An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data
R. King, S. Kodali, C. Krueger, T. Yang, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2024)

[C4] ECG Abnormality Detection Using MIMIC-IV-ECG Data Via Supervised Contrastive Learning
Z. Nowroozilarki, S. Huang, R. Khera, B. J. Mortazavi
Conference of the IEEE EMBS (EMBC) (2024)

[C5] Multimodal Pretraining of Medical Time Series and Notes
R. King, T. Yang, B. J. Mortazavi
Machine Learning for Health (ML4H) (2023)

[C6] Predicting Real-time, Recurrent Adverse Invasive Ventilation from Clinical Data Streams
A. Pakbin, Z. Nowroozilarki, Donald K. K. Lee, B. J. Mortazavi
IEEE 19th International Conference on Body Sensor Networks (BSN) (2023)

[C7] Earlier identification of hypertensive events in a telemonitoring system
E. Do, S. Lavu, H. Kum, B. J. Mortazavi
IEEE 19th International Conference on Body Sensor Networks (BSN) (2023)

[C8] Modeling the effect of non-exercise activity on peak post-prandial glucose in diabetes
E. Do, A. Das, N. Glanz, W. Bevier, R. Santiago, D. Kerr, R. Gutierrez-Osuna, B. J. Mortazavi
IEEE 19th International Conference on Body Sensor Networks (BSN) (2023)

[C9] ArterialNet: Arterial Blood Pressure Reconstruction
S. Huang, R. Jafari, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2023) Oral Acceptance Rate 12%

[C10] Joint Embedding of Food Photographs and Blood Glucose for Improved Calorie Estimation
L. Zhang, S. Huang, A. Das, E. Do, N. Glantz, W. Bevier, R. Santiago, D. Kerr, R. Gutierrez-Osuna, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2023) Oral Acceptance Rate 12%

[C11] Clinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity
L. Zhang, R. Khera, B. J. Mortazavi
45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2023)

[C12] Semi-supervised Meta-learning for Multi-source Heterogeneity in Time-series Data
L. Zhang, B. J. Mortazavi
Machine Learning for Healthcare (MLHC) (2023)

[C13] Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Z. Huo, X. Qian, S. Huang, Z. Wang, B. J. Mortazavi
Machine Learning for Healthcare (MLHC) (2022)

[C14] Enhancing Continuous Glucose Monitoring-based Eating Detection with Wearable Biomarkers
S. Omidvar, A. R. Roghanizad, L. Chikwetu, G. Ash, J. Dunn and B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2022) Acceptance Rate 30.2%

[C15] Dynimp: Dynamic Imputation for Wearable Sensing Data through Sensory and Temporal Relatedness
Z. Huo, T. Ji, Y. Liang, S. Huang, Z. Wang, X. Qian, B. J. Mortazavi
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022) Acceptance Rate 45.0%

[C16] A Metric Learning Approach for Personalized Meal Macronutrient Estimation from Postprandial Glucose Response Signals
M. Yang, P. Paromita, T. Chaspari, A. Das, S. Sajjadi, B. J. Mortazavi, R. Gutierrez-Osun
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2021) Acceptance Rate 32.7%

[C17] DynEHR: Dynamic adaptation of models with data heterogeneity in electronic health records
L. Zhang, X. Chen, T. Chen, Z. Wang, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2021) Acceptance Rate 32.7%

[C18] Sparse Gated Mixture-of-Experts to Separate and Interpret Patient Heterogeneity in EHR data
Z. Huo, L. Zhang, R. Khera, S. Huang, X. Qian, Z. Wang, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2021) Acceptance Rate 32.7%

[C19] Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards
Z. Nowroozilarki, A. Pakbin, J. Royalty, D. K. K. Lee, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2021) Acceptance Rate 32.7%

[C20] Outcomes-Driven Clinical Phenotyping in Cardiogenic Shock using a Mixture of Experts
N. C. Hurley, A. Berkowitz, F. Masoudi, J. Ross, N. Desai, N. Shah, S. Dhruva, B. J. Mortazavi
IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (2021) Acceptance Rate 32.7%

[C21] A Sparse Coding Approach to Automatic Diet Monitoring with Continuous Glucose Monitors
A. Das, S. Sajjadi, B. Mortazavi, T. Chaspari, P. Paromita, L. Ruebush, N. Deutz, R. Gutierrez-Osuna
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021) Acceptance Rate 48.0%

[C22] Towards The Development of Subject-Independent Inverse Metabolic Models
S. Sajjadi, A. Das, R. Gutierrez-Osuna, T. Chaspari, P. Paromita, L. E. Ruebush, N. E. Deutz, B. J. Mortazavi
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021) Acceptance Rate 48.0%

[C23] Developing Personalized Models of Blood Pressure Estimation with Wearable Sensors Using Minimally-Trained Domain Adversarial Neural Networks
L. Zhang, N. C. Hurley, B. Ibrahim, E. Spatz, H. Krumholz, R. Jafari, B. J. Mortazavi
Machine Learning for Healthcare (MLHC 2020) Acceptance Rate 35%

[C24] Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
J. Lovelace, N. C. Hurley, A. Haimovich, B. J. Mortazavi
Machine Learning for Healthcare (MLHC 2020) Acceptance Rate 35%

[C25] BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Wang, X., A. Pakbin, B. J. Mortazavi, Zhao, H., Lee, D.K.K.
International Conference on Machine Learning (ICML 2020) Acceptance Rate 27%

[C26] Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Z. Huo, A. Pakbin, Chen, X., N. C. Hurley, Yuan, Y., Qian, X., Wang, Z., Huang, S., B. J. Mortazavi
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AIStats 2020) Acceptance Rate 29%

[C27] Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
N. C. Hurley, A. Haimovich, Taylor, R., B. J. Mortazavi
Elsevier Smart Health. (Special Issue: Proceedings of the ACM/IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies

[C28] Sparse Gated Mixture-of-Experts to Separate andInterpret Patient Heterogeneity in EHR data
Z. Huo, Zhang L., Khera, R., Huang, S., Qian, X., Wang, Z., B. J. Mortazavi
In Proceedings of the IEEE Conference on Biomedical and Health Informatics (BHI ’20) (Acceptance Rate: 33%)

[C29] DynEHR: Dynamic adaptation of models with data heterogeneity in electronic health records
Zhang L., Chen, X., Chen, T., Wang, Z., B. J. Mortazavi
In Proceedings of the IEEE Conference on Biomedical and Health Informatics (BHI ’20) (Acceptance Rate: 33%)

[C30] Reinforcement Learning using EEG signals for Therapeutic Use of Music in Emotion Management
Dutta, E., Bothra, A., T. Chaspari, Ioerger, T., B. J. Mortazavi
Proceedings of the 42nd International Conference of the IEEE Engineering and Medicine in Biology Society (EMBC 2020)

[C31] Sparse Embedding for Interpretable Hospital Admission Prediction.
Huo, Z, Sundararajhan, H, N. C. Hurley, A. Haimovich, Taylor, R., B. J. Mortazavi
In Proceedings of the 41st International Conference of the IEEE Engineering and Medicine in Biology Society (EMBC 2019)

[C32] Predicting the meal macronutrient composition from continuous glucose monitors
Z. Huo, B. J. Mortazavi, T. Chaspari, Deutz, N., Reubush, L., R. Gutierrez-Osuna
In Proceedings of the IEEE Conference on Biomedical and Health Informatics (BHI ’19) (Acceptance Rate: 31%, Oral Paper Acceptance Rate: 11%)

[C33] A Human-centered Wearable Sensing Platform with Intelligent Automated Data Annotation Capabilities
R. Solis, A. Pakbin, A. Akbari, B. J. Mortazavi, R. Jafari
ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI) (Acceptance Rate: 35%)

[C34] Tagging Wearable Accelerometers in Camera Frames through Information Translation between Vision Sensors and Accelerometers
A. Akbari, Liu, P., B. J. Mortazavi, R. Jafari
International Conference on Cyber Physical Systems (ICCPS) (Acceptance Rate: 22%)

[C35] Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
Ardiwibowo, R., Zhao, G., Wang, Z., B. J. Mortazavi, Huang, S., Qian, X.
AIStats 2019 (Acceptance Rate: 32%)

[C36] Interactive Dimensionality Reduction for Improving Patient Adherence in Remote Health Monitoring
Kalatzis, A., B. J. Mortazavi, Pourhomayoun, M
In Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI)

[C37] Prediction of ICU Readmissions Using Data at Patient Discharge
A. Pakbin, Rafi, P., N. C. Hurley, Schulz., W., H. Krumholz, B. J. Mortazavi
In Proceedings of the 40th International Conference of the IEEE Engineering and Medicine in Biology Society

[C38] Monitoring Lung Mechanics during Mechanical Ventilation using Machine Learning Algorithms
Hezarjaribi, N., Dutta, R., Xing, T., Murdoch, G., Mazrouee, S., B. J. Mortazavi, Ghassemzadeh, H.
In Proceedings of the 40th International Conference of the IEEE Engineering and Medicine in Biology Society

[C39] Knowledge-Driven Dictionaries for Sparse Representation of Continuous Glucose Monitoring Signals
Goel, N., T. Chaspari, B. J. Mortazavi, Prioleau, T., Sabharwal, A., R. Gutierrez-Osuna
In Proceedings of the 40th International Conference of the IEEE Engineering and Medicine in Biology Society

[C40] Context-Aware Analytics for Activity Recognition
Pourhomayoun, M., Nemati, E., B. J. Mortazavi, M. Sarrafzadeh
In Proceedings of the Fourth International Conference on Data Analytics (Data Analytics 2015) (Best Paper Award)

[C41] Impact of Sensor Misplacement on Estimating Metabolic Equivalent of Task with Wearables
Alinia, P., Saeedi, R., B. J. Mortazavi, H. Ghasemzadeh
In Proceedings of the 12th IEEE Conference on Wearable and Implantable Body Sensor Networks (BSN 2015)

[C42] Multiple Model Recognition for Near-Realistic Exergaming
B. J. Mortazavi, Pourhomayou, M., Nyamathi, S., Wu, B., S. I. Lee, M. Sarrafzadeh
In Proceedings of the 2015 IEEE Interantional Conference on Pervasive Computing and Communication (PerCom)

[C43] Support Vector Regression for Estimating the Metabolic Equivalent of Task of Exergaming Actions
B. J. Mortazavi, Pourhomayoun, M., Alshurafa, N., Chronley, M., S. I. Lee, C. RobertsK., M. Sarrafzadeh
In Proceedings the Conference on Healthcare Innovations and Point-of-Care Technologies. (IEEE EMBS HIPT)

[C44] Multiple Model Analytics for Adverse Event Prediction in Remote Health Monitoring Systems
Pourhomayoun, M., Alshurafa, N., B. J. Mortazavi, H. Ghasemzadeh, M. Sarrafzadeh
In Proceedings of the Conference on Healthcare Innovations and Point-of-Care Technologies. (IEEE EMBS HIPT)

[C45] User-Centric Exergaming with Fine-Grain Activity Recognition: A Dynamic Optimization Approach
B. J. Mortazavi, S. I. Lee, M. Sarrafzadeh
In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (Ubicomp ’14 Adjunct)

[C46] Determining the Single Best Axis for Exercise Repetition Recognition and Counting in SmartWatches
B. J. Mortazavi, Pourhomayoun, M., Alsheikh, G., Alshurafa, N., S. I. Lee, Sarafzadeh, M.
Proceedings of the 11th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN 2014)

[C47] Anti-Cheating: Detecting Self-Inflicted and Impersonator Cheaters for Remote Health Monitoring Systems with Wearable Sensors
N. Alshurafa, M. Pourhomayoun, S. Nyamathi, L. Bao, B. J. Mortazavi, Eastwood, J., M. Sarrafzadeh
In Proceedings of the 11th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN 2014)

[C48] Remote Monitoring Systems: What Impact Can Data Analytics Have on Cost?
S. I. Lee, H. Ghasemzadeh, B. J. Mortazavi, M. Lan, M. Ong, M. Sarrafzadeh
In Proceedings of Wireless Health 2013

[C49] MET Calculations from On-Body Accelerometers for Exergaming Movements
B. J. Mortazavi, Alsharufa, N., S. I. Lee, Lan, M., Chronley, M., C. RobertsK., M. Sarrafzadeh
In Proceedings of the 10th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN 2013)

[C50] High Performance Multi-Dimensional Signal Search with Applications in Remote Medical Monitoring
M. Moazeni, B. J. Mortazavi, M. Sarrafzadeh
In Proceedings of the 10th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN 2013)

[C51] Robust Human Intensity-Varying Activity Recognition using Stochastic Approximation in Wearable Sensors.
Alsharufa, N., Xu, W., Liu, J., Huang, M.C., B. J. Mortazavi, C. RobertsK., M. Sarrafzadeh
In Proceedings of the 10th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN 2013)

[C52] Objective Assessment of Overexcited Hand Movements Using a Lightweight Sensory Device
S. I. Lee, H. Ghasemzadeh, B. J. Mortazavi, A. Yew, Getachew, R., Razaghy, M., Ghalehsari, N., Paak, B., Garst, J., Espinal, M., Kimball, J., Lu, D., M. Sarrafzadeh
In Proceedings of the 10th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN 2013)

[C53] A Monte Carlo Approach to Biomedical Time Series Search
J. Woodbridge, B. J. Mortazavi, M. Sarrafzadeh, Bui, A.
In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012)

[C54] Aggregated Indexing of Biomedical Time-Series Data
J. Woodbridge, B. J. Mortazavi, Bui, A., M. Sarrafzadeh
In Proceedings of the 2nd IEEE Conference on Healthcare Informatics, Imaging, and Systems Biology (HISB 2012)

[C55] Dynamic Task Optimization in Remote Diabetes Monitoring Systems
D. M. K. Suh, J. Woodbridge, Moin, T., Lan, M., Alshurafa, N., Samy, L., B. J. Mortazavi, H. Ghasemzadeh, Bui, A., Ahmadi, S., M. Sarrafzadeh
In Proceedings of the 2nd IEEE Conference on Healthcare Informatics, Imaging, and Systems Biology (HISB 2012)

[C56] High Performance Biomedical Time-Series Indexing Using Salient Segmentation
J. Woodbridge, B. J. Mortazavi, M. Sarrafzadeh
In Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC ’12)

[C57] Near-Realistic Motion Video Games with Enforced Activity
B. J. Mortazavi, Chu, K.C., Li, X., Tai, J., Kotekar, S., M. Sarrafzadeh
International Conference on Wearable and Implantable Body Sensor Networks (BSN 2012)

[C58] A Wireless Body-Wearable Sensor System of Designing Physically Interactive Video Games
B. J. Mortazavi, Hagopian, H., J. Woodbridge, Yadegar, B., M. Sarrafzadeh
Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES 2011)

Patents

[P1] Predicting food macronutrients from blood biomarkers
R. Gutierrez-Osuna, B. J. Mortazavi, Z. Huo, G. L. Cote, N. E. Deutz
US Patent US20200352481A1

[P2] Near-Realistic Sports Motion Analysis and Activity Monitoring
M. Sarrafzadeh, B. J. Mortazavi, X. Li, K. Chu
US Patent US10201746B1