Certified AI Triage of ICU Alarms

2026-09-10

AI Summary

针对ICU心律失常报警误报率高的问题，提出一种三向分流方法（保留、抑制或延迟），并通过统计分析确保真实报警被错误抑制的比例低于设定阈值。

Abstract

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.

Problem

• AI Triage
• ICU Alarms
• False Alarms
• Ventricular Tachycardia
• Alarm Reduction

Innovation

• three-way triage
• confidence bound
• alarm reduction
• false alarm suppression