Publications

Neonatal Risk Modeling and Prediction
We propose an automated risk prediction system that can make recommendations to clinicians in real-time with machine learning classifiers that predict the risk of developing neurological impairment.
IEEE-EMBS International Conference on Body Sensor Networks: Sensor and Systems for Digital Health (BSN'23)
Abdullah Mamun, Chia-Cheng Kuo, David W. Britt, Lawrence D. Devoe, Mark I. Evans, Hassan Ghasemzadeh, Judith Klein-Seetharaman
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Neonatal Risk Modeling and Prediction
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence
arXiv preprint
Parker Seegmiller, Joseph Gatto, Abdullah Mamun, Hassan Ghasemzadeh, Diane Cook, John Stankovic, Sarah Masud Preum
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ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence
Multimodal Time-Series Activity Forecasting for Adaptive Lifestyle Intervention Design
We focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance.
IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2022
Abdullah Mamun, Krista S. Leonard, Matthew P. Buman, Hassan Ghasemzadeh
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Multimodal Time-Series Activity Forecasting for Adaptive Lifestyle Intervention Design
Designing Deep Neural Networks Robust to Sensor Failure in Mobile Health Environments
We propose an algorithm that can reconstruct the missing input data for unavailable sensors.
IEEE Engineering in Medicine and Biology Conference (EMBC), 2022
Abdullah Mamun, Seyed Iman Mirzadeh, Hassan Ghasemzadeh
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Designing Deep Neural Networks Robust to Sensor Failure in Mobile Health Environments