{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["38(4)"],"submitter":["Spijkerboer FL"],"funding":["Medtronic"],"pubmed_abstract":["Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. We used data collected during the observational, prospective PRODIGY trial, in which patients receiving parenteral opioids underwent continuous capnography and pulse oximetry monitoring while on the general care floor [1]. Abnormal ventilation segments in the data stream were reviewed by nine experts and inter-rater agreement was assessed. Abnormal segments were defined as th"],"journal":["Journal of clinical monitoring and computing"],"pagination":["915-925"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11297897"],"repository":["biostudies-literature"],"pubmed_title":["A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring."],"pmcid":["PMC11297897"],"pubmed_authors":["Spijkerboer FL","Overdyk FJ","Dahan A"],"additional_accession":[]},"is_claimable":false,"name":"A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring.","description":"Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. We used data collected during the observational, prospective PRODIGY trial, in which patients receiving parenteral opioids underwent continuous capnography and pulse oximetry monitoring while on the general care floor [1]. Abnormal ventilation segments in the data stream were reviewed by nine experts and inter-rater agreement was assessed. Abnormal segments were defined as th","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Aug","modification":"2026-06-01T22:18:14.08Z","creation":"2026-05-22T03:08:01.584Z"},"accession":"S-EPMC11297897","cross_references":{"pubmed":["38619716"],"doi":["10.1007/s10877-024-01155-0"]}}