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Standalone LLM and a Pre-specified Agentic Pipeline for Explaining ICU Mortality Predictions: a Feasibility Study on the eICU Demo Dataset
Not provided in the abstract
explainabilityhealthcare AImortality predictionagentic pipelines
2608.26109
Builder Relevance
1h ago70%
Abstract
This study explores the feasibility of using large language models and agentic pipelines to enhance the explanation of ICU mortality predictions.
Reality Card
Core Claim
The agentic pipeline improves safety-relevant grounding and patient-specific detail in ICU mortality predictions compared to standalone LLMs.
Method / Result
The agentic pipeline achieved higher guideline grounding (0.762) compared to the standalone LLM (0.143).
Limitations
The study's findings may be limited by the specific dataset used (eICU Demo Dataset) and the need for attribution-based checks before clinical application.
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