Hierarchical Time-Series Representation Learning for Medical AI
Date:
This conference presentation covers advances in hierarchical time-series representation learning with applications to multi-domain EEG modeling for cognitive-load classification and ICU outcome prediction.
Abstract: We present novel approaches to learning hierarchical representations from medical time-series data, with specific focus on EEG signals and clinical monitoring data. The proposed methods demonstrate significant improvements in cognitive load classification and ICU outcome prediction tasks.
Technical Content:
- Hierarchical representation learning architectures
- Multi-domain adaptation for EEG signals
- Transformer-based approaches for time-series modeling
- Clinical validation and deployment considerations
Results show substantial improvements over baseline methods, with potential for real-world clinical deployment in cognitive assessment and critical care monitoring systems.utorial 1 on Relevant Topic in Your Field” collection: talks type: “Tutorial” permalink: /talks/2013-03-01-tutorial-1 venue: “UC-Berkeley Institute for Testing Science” date: 2013-03-01 location: “Berkeley, CA, USA” —
This is a description of your tutorial, note the different field in type. This is a markdown files that can be all markdown-ified like any other post. Yay markdown!