Can we generate portable representations for clinical time series data using LLMs?
Can we generate portable representations for clinical time series data using LLMs? explores Leverage LLMs to create portable patient embeddings from clinical time series data, enabling scalable deployment of predictive models across hospitals with reduced retraining.. Commercial viability score: 7/10 in Medical AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkLLMs can construct powerful representations and streamline sample-efficient supervised learningViability: 7.0
- Prior WorkCountering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model MergingViability: 7.0
- Higher ViabilityPVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language ModelsViability: 8.0
- Higher ViabilityThe Patient is not a Moving Document: A World Model Training Paradigm for Longitudinal EHRViability: 8.0
- Competing Approach