Certified AI Triage of ICU Alarms

• URL: http://arxiv.org/abs/2609.12365v1
• Date: Fri, 11 Sep 2026 02:34:40 GMT
• Title: Certified AI Triage of ICU Alarms
• Abstract summary: In the VTaC benchmark 71% of ventricular-tachycardia alarms are false, but silencing a real one can delay recognition of a dangerous arrhythmia.<n>We reframe alarm reduction as three-way triage (retain, suppress, or defer) and bound the decision this analysis treats as harmful.
• Score: 0.7476901945542384
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Bayesian Correlated Equilibrium for Early Insider-Threat Detection [11.643788980306235]
  We model insider threat detection as a dynamic Bayesian game in which a platform coordinates a committee of strategic certifiers to sustain equilibrium among honest users.<n>On CERT r6.2 our mechanism achieves up to 28.3% pre-exfiltration detection with false positives below 1.6%, while both a transformer baseline and a streaming provenance approximation achieve near-zero pre-exfiltration detection.
  arXiv  Detail & Related papers  (2026-09-02T19:12:56Z)
• Repeated-Game Security for Restaking-Based Verifiable Inference [2.9661360665913574]
  This paper shows that a one-round slashing condition can overstate security when inference is supplied repeatedly under the same stake.<n>We propose a deployable mechanism combining history-dependent challenges, reputation-weighted slashing, and stake vesting.
  arXiv  Detail & Related papers  (2026-08-10T03:03:56Z)
• Cybersecurity Detection Classification with Reasoning-enabled Language Models [50.72675435043546]
  A major issue in Security Operations Centers (SOCs) is alert fatigue.<n>Prior work prompts or fine-tunes large language models (LLMs) to emit a triage label directly, but does not train them to reason about whether a detection is a genuine threat.<n>We train a chain-of-thought (CoT) reasoning-enabled triage classifier on real, human-labeled Windows endpoint detections.
  arXiv  Detail & Related papers  (2026-07-30T16:22:13Z)
• Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark [2.5130933318193915]
  Mondrian CP restores valid minority-class coverage, achieving an average minority-coverage improvement of 61.7 percentage points over marginal CP.<n>We quantify dataset-specific break-even thresholds at which deferring ambiguous instances to human experts becomes cost-effective.
  arXiv  Detail & Related papers  (2026-07-29T17:15:33Z)
• Level, Sharpness, and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer [36.02174685737611]
  We find that detector rankings reverse across deployments, every detector exceeds $80%$ FPR95 on at least one domain, and the preferred detector depends on both the in-distribution data and the underlying VLM.<n>We introduce the Complementary Evidence Guard (CEG), a detector-agnostic wrapper that preserves complementary evidence through a non-compensatory fusion of the base detector, level, and sharpness.
  arXiv  Detail & Related papers  (2026-07-29T08:00:49Z)
• Certified Speculative Execution for Untrusted AI Agents [5.864346468963738]
  Certificate-Gated Prefix Acceptance (CGPA) is a speculative-execution contract for untrusted AI agents.<n>On a deployment-scale unit-commitment instance it turns a frozen 8B LLM into a 2.96x per-episode wall-clock speedup at 2.1% regret, outpacing the domain (1.79x) and a safe receding-horizon baseline (1.07x)
  arXiv  Detail & Related papers  (2026-06-30T01:45:56Z)
• False Sense of Safety in Selective Signal Classification: Auditing Bound Tightness and Exchangeability for Risk Control [6.3999417080764225]
  Selective prediction with distribution-free risk control promises that, with confidence 1-delta over the calibration draw, the error rate of accepted inputs stays below a user budget alpha.<n>We audit this promise on signal-domain detectors -- machine anomalous-sound detection (ASD) and AI-generated-image forensics.
  arXiv  Detail & Related papers  (2026-06-13T06:48:50Z)
• Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
  Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
  arXiv  Detail & Related papers  (2026-05-20T19:08:38Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• TRUST: A Framework for Decentralized AI Service v.0.1 [47.384270414446604]
  Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification.<n>We introduce TRUST (Transparent, Robust, and Unified Services for Trustworthy AI), a decentralized framework with three innovations.<n>We prove a Safety-Profitability Theorem ensuring honest auditors profit while malicious actors incur losses.
  arXiv  Detail & Related papers  (2026-04-29T19:32:58Z)
• From Black Box to Glass Box: Cross-Model ASR Disagreement to Prioto Review in Ambient AI Scribe Documentation [43.148402136307716]
  Cross-model disagreement among heterogeneous ASR systems can act as a reference-free uncertainty signal.<n>We transcribed 50 publicly available medical audio clips with eight ASR systems spanning commercial APIs and open-source engines.<n>Low-agreement regions were enriched for content disagreements, with the content fraction increasing from 53.9% to 73.9% across quintiles of high-risk mass.
  arXiv  Detail & Related papers  (2026-03-02T13:02:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.