Metraipolis › archive › dc_5685705fdbbc

A statistical framework for identifying subgroup vulnerability to predictive multiplicity in clinical AI — arXiv:2609.37064

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37064
License (as archived): CC0-1.0
Archived: 2026-10-07T18:00:50.161Z
SHA-256 of the archived text: b9c789fa370f7421231040fd11a86441e49f5e20ad408bf682ed973d5751a59b
Stable URL of this page: https://metraipolis.com/documento/dc_5685705fdbbc

How to cite

A statistical framework for identifying subgroup vulnerability to predictive multiplicity in clinical AI — arXiv:2609.37064 — arXiv (en), https://arxiv.org/abs/2609.37064 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5685705fdbbc (Metraipolis archive dc_5685705fdbbc, sha256 b9c789fa370f7421231040fd11a86441e49f5e20ad408bf682ed973d5751a59b)

BibTeX

@misc{dc5685705fdbbc,
  title = {A statistical framework for identifying subgroup vulnerability to predictive multiplicity in clinical AI — arXiv:2609.37064},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37064},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5685705fdbbc; sha256 b9c789fa370f7421231040fd11a86441e49f5e20ad408bf682ed973d5751a59b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37064. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

AI models trained on the same data can disagree about patient risk, with disagreement potentially concentrated in clinically important subgroups. We propose V(S), a statistically grounded vulnerability index combining an observable lower-bound witness of model disagreement with clinical severity, and develop inference and multiplicity-adjustment procedures for auditing prespecified subgroups. We applied the framework to two large critical-care cohorts, MIMIC-IV (n = 65,078) for model development and eICU-CRD (n = 188,230 admissions, 208 hospitals) for external validation, comparing a random forest with logistic regression across 158 prespecified subgroups. The two primary models did not both satisfy the prespecified epsilon = 0.02 Rashomon-set tolerance: the logistic-regression AUC was 0.0488 below the best candidate-model AUC. The RF-LR discrimination gap is thus interpreted as disagreement between two specific models, not as a guaranteed lower bound on the full Rashomon set. Nine subgroups had discrimination gaps distinguishable from a prespecified clinical floor. The age >=80 and cardiac subgroup had the largest point estimate of V(S) (0.307), but was underpowered and did not meet the full high-priority decision rule. The univariate cardiac subgroup (V(S) = 0.193, 95% CI [0.163, 0.223]) was the only statistically distinguishable subgroup with adequate power. Post hoc analyses identified lactate as important for both models but did not establish a causal explanation for the disagreement. Four simulation studies quantified operating characteristics of the proposed procedures, including inflated small-sample detection rates and imperfect Wald-interval coverage. The framework offers a reproducible approach for ranking subgroup vulnerability to model disagreement while separating exploratory signals from adequately supported findings.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in stat.ME).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.37064 — 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 2026-10-07T17:33:58.755Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).