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Certified AI Triage of ICU Alarms

arXiv机器学习 2026-09-11 10:34 9 阅读 查看原文

In the VTaC benchmark 71% of ventricular-tachycardia alarms are false, but silencing a real one can delay recognition of a dangerous arrhythmia.

We reframe alarm reduction as three-way triage (retain, suppress, or defer) and bound the decision this analysis treats as harmful: among suppressed alarms, the fraction that were genuine stays below a user-set budget with 95% confidence, under i.i.d. event sampling.

Alarms sharing a waveform record are dependent, so the clustered analysis is a sensitivity check.

On the official split a 5% budget certifies in all three seeds, suppressing 74.8% of false alarms while silencing 1.5% of genuine ones, at AUROC 0.953 and Challenge Score 83.33, numerically comparable to the strongest of the eleven published systems.

Our central finding measures what multiplicity costs: the correction charges for every candidate, so a finer grid can certify strictly less.

Under held-out calibration the 885-cell grid we declared certifies 1 of 15 fold-runs, while choosing the grid on a separate selection partition certifies 8.

We project the calibration volume each budget needs, making an uncertifiable budget a design parameter.

Finally, adding a learned reliability dimension to the policy grid did not sharpen the certified frontier.