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LLM vs. Agentic Pipeline for ICU Mortality Prediction Explanations

By PulseAugur Editorial · [1 sources] ·

A feasibility study explored the use of a standalone Large Language Model (LLM) versus a multi-step agentic pipeline for explaining Intensive Care Unit (ICU) mortality predictions. The study found that while both methods could predict mortality with reasonable accuracy, the agentic pipeline demonstrated improved safety by avoiding explicit outcome leakage and showing better guideline grounding and value specificity. However, the standalone LLM showed higher alignment with SHAP values and direction consistency, suggesting a trade-off between different explanation qualities. AI

IMPACT Agentic pipelines may offer safer, more grounded explanations for high-stakes AI applications like medical predictions.

RANK_REASON Research paper on LLM application in healthcare explanations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM vs. Agentic Pipeline for ICU Mortality Prediction Explanations

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24 / 100
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Research paper on LLM application in healthcare explanations. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety, model release
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Di Zhu, Chen Xie, Haoyun Zhang, Zihan Wei, Ziwei Wang, Jiazhao Shi, Ziyu Wang, Qiyang Xie ·

    Standalone LLM and a Pre-specified Agentic Pipeline for Explaining ICU Mortality Predictions: a Feasibility Study on the eICU Demo Dataset

    arXiv:2608.26109v1 Announce Type: new Abstract: Machine-learning models can predict ICU mortality accurately, but feature-attribution methods alone rarely provide the clinical narrative needed for bedside use. Large language models (LLMs) may bridge this gap, and multi-step agent…

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