biostudies-literatureUnknown165(5)Lilly CM<h4>Background</h4>Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur.<h4>Research question</h4>Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of intubation or administration of vasopressors?<h4>Study design and methods</h4>An ML algorithm was trained to predict intubation and vasopressor initiation events among critically ill adults. Its performance was compared with BM alarms and TS alerts.<h4>Results</h4>ML notifications were substantially more accurate and precise, with 50-fold lower alarm burden than TS alerts for predictiChest1139-1148https://www.ebi.ac.uk/biostudies/studies/S-EPMC11214904biostudies-literatureApplication of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults.PMC11214904Chen OLilly CMLipsky AKirk DLieder IBlum JMCucchi EWPessach IMLotun GCelniker GfalseApplication of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults.<h4>Background</h4>Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur.<h4>Research question</h4>Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of intubation or administration of vasopressors?<h4>Study design and methods</h4>An ML algorithm was trained to predict intubation and vasopressor initiation events among critically ill adults. Its performance was compared with BM alarms and TS alerts.<h4>Results</h4>ML notifications were substantially more accurate and precise, with 50-fold lower alarm burden than TS alerts for predicti2024-01-01T00:00:00Z2024 May2025-07-12T03:06:00.463Z2025-07-12T03:06:00.463ZS-EPMC112149043792329210.1016/j.chest.2023.10.036