AI RESEARCH
SigmaMedStat: Temporal Signal Modeling for ICU False Alarm Reduction
arXiv CS.LG
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ArXi:2605.29236v1 Announce Type: new Alarm fatigue in intensive care units (ICUs) is a well documented patient safety crisis. Clinical monitors generate 350 or alarms per patient per day, out of which 72-99% are clinically irrelevant. Staff desensitization to non-actionable alarms increases the risk of missed true emergencies. This paper presents SigmaMedStat, a machine learning system that evaluates the trustworthiness of physiological alarm signals before clinical action is taken.