A daytime obstructive sleep apnea severity assessment framework.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by repeated episodes of complete or partial blockage of the upper airway. These episodes can interfere with sound sleep and have fatal health consequences. They can also reduce the flow of oxygen to vital organs, like the brain, and lead to brain damage.

A gender-aware framework for the daytime detection of obstructive sleep apnea.

Sleep is an activity that is necessary for our survival. While the body may be still during sleep, the brain is actively progressing through repeating cycles of light and deep sleep whose purpose is physical and mental recovery and regeneration. Obstructive sleep apnea (OSA) is a sleep disorder in which breathing is frequently and repeatedly stopped during sleep.

A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.

In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems.

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring.