By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FUSE-MET optimizes label merging, reducing label complexity and improving classification accuracy.
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.
We focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance.
We propose an algorithm that can reconstruct the missing input data for unavailable sensors.