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.
Our patient-independent model achieved an overall accuracy of 78% in detecting FoG events using both medication ‘On’ and ‘Off’ state data.
Lung cancer and bladder cancer can be causally linked, so distinguishing between lung and bladder cancer tissues is critical for accurate diagnosis. The goal of this study was to determine the best method for classifying these tissues based on gene expression analysis data.
We focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance.