FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2065050289> ?p ?o ?g. }

• W2065050289 endingPage "114" @default.
• W2065050289 startingPage "108" @default.
• W2065050289 abstract "To date, no studies have attempted to utilize data from a combination of vital signs, heart rate variability and complexity (HRV, HRC), as well as machine learning (ML), for identifying the need for lifesaving interventions (LSIs) in trauma patients. The objectives of this study were to examine the utility of the above for identifying LSI needs and compare different LSI-associated models, with the hypothesis that an ML model would be superior in performance over multivariate logistic regression models. One hundred four patients transported from the injury scene via helicopter were selected for the study. A wireless vital signs monitor was attached to the patient’s arm and used to capture physiologic data, including HRV and HRC. The power of vital sign measurements, HRV, HRC, and Glasgow Coma Scale score (GCS) to identify patients requiring LSIs was estimated using multivariate logistic regression and ML. Receiver operating characteristic (ROC) curves were also obtained. Thirty-two patients underwent 75 LSIs. After logistic regression, ROC curves demonstrated better identification for LSIs using heart rate (HR) and HRC (area under the curve [AUC] of 0.81) than using HR alone (AUC of 0.73). Likewise, ROC curves demonstrated better identification for LSIs using GCS and HRC (AUC of 0.94) than using GCS and HR (AUC of 0.92). Importantly, ROC curves demonstrated that an ML model using HR, GCS, and HRC (AUC of 0.99) had superior performance over multivariate logistic regression models for identifying the need for LSIs in trauma patients. Development of computer decision support systems should utilize vital signs, HRC, and ML in order to achieve more accurate diagnostic capabilities, such as identification of needs for LSIs in trauma patients." @default.
• W2065050289 created "2016-06-24" @default.
• W2065050289 creator A5027352338 @default.
• W2065050289 creator A5042251160 @default.
• W2065050289 creator A5056060130 @default.
• W2065050289 creator A5060399252 @default.
• W2065050289 creator A5091767756 @default.
• W2065050289 date "2014-08-01" @default.
• W2065050289 modified "2023-10-14" @default.
• W2065050289 title "Utility of Vital Signs, Heart Rate Variability and Complexity, and Machine Learning for Identifying the Need for Lifesaving Interventions in Trauma Patients" @default.
• W2065050289 cites W1571411961 @default.
• W2065050289 cites W1862394037 @default.
• W2065050289 cites W1984474176 @default.
• W2065050289 cites W1985789517 @default.
• W2065050289 cites W1986000496 @default.
• W2065050289 cites W1987506814 @default.
• W2065050289 cites W1990827987 @default.
• W2065050289 cites W2006940548 @default.
• W2065050289 cites W2023453742 @default.
• W2065050289 cites W2027351503 @default.
• W2065050289 cites W2027998133 @default.
• W2065050289 cites W2034828191 @default.
• W2065050289 cites W2037089859 @default.
• W2065050289 cites W2054121171 @default.
• W2065050289 cites W2069340802 @default.
• W2065050289 cites W2086882405 @default.
• W2065050289 cites W2118183148 @default.
• W2065050289 cites W2127784306 @default.
• W2065050289 cites W2144488582 @default.
• W2065050289 cites W2277879619 @default.
• W2065050289 cites W2285072859 @default.
• W2065050289 cites W2395348944 @default.
• W2065050289 cites W4253582190 @default.
• W2065050289 doi "https://doi.org/10.1097/shk.0000000000000186" @default.
• W2065050289 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24727872" @default.
• W2065050289 hasPublicationYear "2014" @default.
• W2065050289 type Work @default.
• W2065050289 sameAs 2065050289 @default.
• W2065050289 citedByCount "44" @default.
• W2065050289 countsByYear W20650502892014 @default.
• W2065050289 countsByYear W20650502892015 @default.
• W2065050289 countsByYear W20650502892016 @default.
• W2065050289 countsByYear W20650502892017 @default.
• W2065050289 countsByYear W20650502892018 @default.
• W2065050289 countsByYear W20650502892019 @default.
• W2065050289 countsByYear W20650502892020 @default.
• W2065050289 countsByYear W20650502892021 @default.
• W2065050289 countsByYear W20650502892022 @default.
• W2065050289 countsByYear W20650502892023 @default.
• W2065050289 crossrefType "journal-article" @default.
• W2065050289 hasAuthorship W2065050289A5027352338 @default.
• W2065050289 hasAuthorship W2065050289A5042251160 @default.
• W2065050289 hasAuthorship W2065050289A5056060130 @default.
• W2065050289 hasAuthorship W2065050289A5060399252 @default.
• W2065050289 hasAuthorship W2065050289A5091767756 @default.
• W2065050289 hasBestOaLocation W20650502891 @default.
• W2065050289 hasConcept C119857082 @default.
• W2065050289 hasConcept C126322002 @default.
• W2065050289 hasConcept C141071460 @default.
• W2065050289 hasConcept C151956035 @default.
• W2065050289 hasConcept C161584116 @default.
• W2065050289 hasConcept C17624336 @default.
• W2065050289 hasConcept C194828623 @default.
• W2065050289 hasConcept C2776890885 @default.
• W2065050289 hasConcept C2777953023 @default.
• W2065050289 hasConcept C41008148 @default.
• W2065050289 hasConcept C58471807 @default.
• W2065050289 hasConcept C71635504 @default.
• W2065050289 hasConcept C71924100 @default.
• W2065050289 hasConcept C76318530 @default.
• W2065050289 hasConcept C84393581 @default.
• W2065050289 hasConceptScore W2065050289C119857082 @default.
• W2065050289 hasConceptScore W2065050289C126322002 @default.
• W2065050289 hasConceptScore W2065050289C141071460 @default.
• W2065050289 hasConceptScore W2065050289C151956035 @default.
• W2065050289 hasConceptScore W2065050289C161584116 @default.
• W2065050289 hasConceptScore W2065050289C17624336 @default.
• W2065050289 hasConceptScore W2065050289C194828623 @default.
• W2065050289 hasConceptScore W2065050289C2776890885 @default.
• W2065050289 hasConceptScore W2065050289C2777953023 @default.
• W2065050289 hasConceptScore W2065050289C41008148 @default.
• W2065050289 hasConceptScore W2065050289C58471807 @default.
• W2065050289 hasConceptScore W2065050289C71635504 @default.
• W2065050289 hasConceptScore W2065050289C71924100 @default.
• W2065050289 hasConceptScore W2065050289C76318530 @default.
• W2065050289 hasConceptScore W2065050289C84393581 @default.
• W2065050289 hasIssue "2" @default.
• W2065050289 hasLocation W20650502891 @default.
• W2065050289 hasLocation W20650502892 @default.
• W2065050289 hasLocation W20650502893 @default.
• W2065050289 hasOpenAccess W2065050289 @default.
• W2065050289 hasPrimaryLocation W20650502891 @default.
• W2065050289 hasRelatedWork W2008911041 @default.
• W2065050289 hasRelatedWork W2051966709 @default.
• W2065050289 hasRelatedWork W2373132269 @default.
• W2065050289 hasRelatedWork W2390926424 @default.
• W2065050289 hasRelatedWork W2392162854 @default.
• W2065050289 hasRelatedWork W2744023513 @default.

• next