Portable Patient Embeddings via LLM Summarization
Portable Patient Embeddings via LLM Summarization
Auto-generated stub. Edit this file to add more details.
Generating fixed-length, transferrable patient vector representations by first summarizing irregular ICU time series data into natural language using a frozen LLM, and then embedding the text summary.
Why It Matters
This is the core novel technique proposed to address the portability problem in clinical ML deployment.
Evidence
we map from irregular ICU time series onto concise natural language summaries using a frozen LLM, then embed each summary with a frozen text embedding model to obtain a fixed length vector
Related Papers
Metadata & Links
- created_at
- 2026-03-28T05:28:46Z