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In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test-time. The method incrementally asks for features based on the available context that are known feature values. The proposed method is based on sensitivity analysis in neural networks and density estimation using denoising autoencoders with binary representation layers.
Recent studies suggest that epidural stimulation of the spinal cord could increase the motor pattern both in motor and sensory complete spinal cord injury (SCI) patients. This paper presents a novel technique using machine learning methods to predict the functionality of a SCI patient after epidural stimulation.
To address the need for asthma self-management in pediatrics, we present the feasibility of a mobile health (mHealth) platform built on their prior work in an asthmatic adult and child.
Remote and Personalized Systems for Clinical Outcomes Research
Professor, Texas A&M University
Assessment of Movement Disorders in Patients with Degenerative Spinal Cord Disorders
Professor, Massachusetts Amherst