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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 — arXiv:2608.26109

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.26109
License (as archived): CC0-1.0
Archived: 2026-10-17T14:16:03.223Z
SHA-256 of the archived text: 71c70b306390b00316b09757ac8ca245920233f512c2f0885644e1edbe373f65
Stable URL of this page: https://metraipolis.com/documento/dc_7bb34fd7bdd4

How to cite

Standalone LLM and a Pre-specified Agentic Pipeline for Explaining ICU Mortality Predictions: a Feasibility Study on the eICU Demo Dataset — arXiv:2608.26109 — arXiv (en), https://arxiv.org/abs/2608.26109 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7bb34fd7bdd4 (Metraipolis archive dc_7bb34fd7bdd4, sha256 71c70b306390b00316b09757ac8ca245920233f512c2f0885644e1edbe373f65)

BibTeX

@misc{dc7bb34fd7bdd4,
  title = {Standalone LLM and a Pre-specified Agentic Pipeline for Explaining ICU Mortality Predictions: a Feasibility Study on the eICU Demo Dataset — arXiv:2608.26109},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.26109},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7bb34fd7bdd4; sha256 71c70b306390b00316b09757ac8ca245920233f512c2f0885644e1edbe373f65},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.26109. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 agentic pipelines are a plausible extension because they separate data interpretation, guideline checking, and final explanation. This revised feasibility study preserves the original standalone-versus-agentic comparison while making the main clinical findings more explicit. Using the retained local eICU Demo artifact set (2,353 ICU stays; 8.1\% mortality), XGBoost achieved an AUROC of 0.855 (95\% CI 0.796--0.906) and an AUPRC of 0.332 (95\% CI 0.217--0.494). On a stratified 38-case explanation subset, the standalone LLM produced 1 explanation with explicit outcome leakage, whereas the four-step agentic pipeline produced none. Among the 14 cases that overlapped with the SHAP review subset, the standalone LLM showed higher SHAP alignment (mean Jaccard 0.171 versus 0.077) and higher direction consistency (92.9\% versus 78.6\%), while the agentic pipeline showed higher guideline grounding (0.762 versus 0.143), higher value specificity (0.236 versus 0.143), and slightly higher plausibility (0.700 versus 0.671). Clinically, the results suggest that agentic decomposition may improve safety-relevant grounding and patient-specific detail, but it should be paired with attribution-based checks before use in high-stakes risk explanation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-28 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.26109 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T13:16:53.408Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).