Abstract
This paper proposes a novel secular knowledge representation and learning framework to proposed large- scale secular signature mining of longitudinal heterogeneous occasional data. The framework allows the presentation, extra4ction, and mining of high order latent occasion event structure and relationships between single and many sequences. The prescribed data representation maps the heterogeneous sequences to a image by encoding occasions as a structured spatial-secular shape process. We have suggested clinical assessment for naked interactive knowledge discovery in large electronic health record databases.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have