{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["165(5)"],"submitter":["Lilly CM"],"pubmed_abstract":["<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 predicti"],"journal":["Chest"],"pagination":["1139-1148"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11214904"],"repository":["biostudies-literature"],"pubmed_title":["Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults."],"pmcid":["PMC11214904"],"pubmed_authors":["Chen O","Lilly CM","Lipsky A","Kirk D","Lieder I","Blum JM","Cucchi EW","Pessach IM","Lotun G","Celniker G"],"additional_accession":[]},"is_claimable":false,"name":"Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults.","description":"<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 predicti","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2025-07-12T03:06:00.463Z","creation":"2025-07-12T03:06:00.463Z"},"accession":"S-EPMC11214904","cross_references":{"pubmed":["37923292"],"doi":["10.1016/j.chest.2023.10.036"]}}