RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models explores A time-aware LLM framework that integrates structured EHR encoders via prompt tuning to capture longitudinal patient information and population-level patterns for clinical prediction.. Commercial viability score: 7/10 in LLM Applications.
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Canonical route: /paper/reprompt-recurrent-prompt-tuning-for-integrating-structured-ehr-encoders-with-large-language-models
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Agent Handoff
Canonical ID reprompt-recurrent-prompt-tuning-for-integrating-structured-ehr-encoders-with-large-language-models | Route /paper/reprompt-recurrent-prompt-tuning-for-integrating-structured-ehr-encoders-with-large-language-models
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/reprompt-recurrent-prompt-tuning-for-integrating-structured-ehr-encoders-with-large-language-modelsMCP example
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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