FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3040606985> ?p ?o ?g. }

• W3040606985 abstract "Abstract Background Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can achieve superior performance than the stepwise approach in deriving risk stratification models. Methods A retrospective analysis was conducted on the data of patients >20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and HRnV parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization. Candidate variables identified using univariable analysis were then used to generate the stepwise logistic regression model and eight machine learning dimensionality reduction prediction models. A separate set of models was derived by excluding troponin. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance. Results 795 patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods marginally but non-significantly outperformed stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve (AUC) of 0.901. All HRnV-based models generated in this study outperformed several existing clinical scores in ROC analysis. Conclusions HRnV-based models using stepwise logistic regression performed better than existing chest pain scores for predicting MACE, with only marginal improvements using machine learning dimensionality reduction. Moreover, traditional stepwise approach benefits from model transparency and interpretability; in comparison, machine learning dimensionality reduction models are black boxes, making them difficult to explain in clinical practice." @default.
• W3040606985 created "2020-07-10" @default.
• W3040606985 creator A5015053512 @default.
• W3040606985 creator A5030967805 @default.
• W3040606985 creator A5049356493 @default.
• W3040606985 creator A5050248482 @default.
• W3040606985 creator A5058750595 @default.
• W3040606985 creator A5059452714 @default.
• W3040606985 creator A5087685316 @default.
• W3040606985 date "2020-07-06" @default.
• W3040606985 modified "2023-10-02" @default.
• W3040606985 title "Machine learning dimensionality reduction for heart rate n-variability (HRnV) based risk stratification of chest pain patients in the emergency department" @default.
• W3040606985 cites W1501108249 @default.
• W3040606985 cites W1742512077 @default.
• W3040606985 cites W1968032292 @default.
• W3040606985 cites W1994452740 @default.
• W3040606985 cites W1998025025 @default.
• W3040606985 cites W2001141328 @default.
• W3040606985 cites W2002830978 @default.
• W3040606985 cites W2020149073 @default.
• W3040606985 cites W2026699230 @default.
• W3040606985 cites W2030507001 @default.
• W3040606985 cites W2034690624 @default.
• W3040606985 cites W2038622192 @default.
• W3040606985 cites W2041836310 @default.
• W3040606985 cites W2053186076 @default.
• W3040606985 cites W2059137883 @default.
• W3040606985 cites W2075216460 @default.
• W3040606985 cites W2085657320 @default.
• W3040606985 cites W2085811721 @default.
• W3040606985 cites W2094962207 @default.
• W3040606985 cites W2114995492 @default.
• W3040606985 cites W2120596490 @default.
• W3040606985 cites W2124724401 @default.
• W3040606985 cites W2128971608 @default.
• W3040606985 cites W2129374946 @default.
• W3040606985 cites W2148946317 @default.
• W3040606985 cites W2154572047 @default.
• W3040606985 cites W2158997610 @default.
• W3040606985 cites W2274449984 @default.
• W3040606985 cites W2320574955 @default.
• W3040606985 cites W2335377152 @default.
• W3040606985 cites W2339573327 @default.
• W3040606985 cites W2386566797 @default.
• W3040606985 cites W2415588839 @default.
• W3040606985 cites W2466272045 @default.
• W3040606985 cites W2516710956 @default.
• W3040606985 cites W2525648808 @default.
• W3040606985 cites W2526471519 @default.
• W3040606985 cites W2531257548 @default.
• W3040606985 cites W2542074903 @default.
• W3040606985 cites W2563046102 @default.
• W3040606985 cites W2734855280 @default.
• W3040606985 cites W2765432117 @default.
• W3040606985 cites W2785762258 @default.
• W3040606985 cites W2789425779 @default.
• W3040606985 cites W2789438342 @default.
• W3040606985 cites W2890527005 @default.
• W3040606985 cites W2895624434 @default.
• W3040606985 cites W2912002275 @default.
• W3040606985 cites W2950336271 @default.
• W3040606985 cites W3011484826 @default.
• W3040606985 cites W3011571399 @default.
• W3040606985 cites W3016267408 @default.
• W3040606985 cites W3039720325 @default.
• W3040606985 cites W4313169793 @default.
• W3040606985 doi "https://doi.org/10.1101/2020.07.05.20146571" @default.
• W3040606985 hasPublicationYear "2020" @default.
• W3040606985 type Work @default.
• W3040606985 sameAs 3040606985 @default.
• W3040606985 citedByCount "0" @default.
• W3040606985 crossrefType "posted-content" @default.
• W3040606985 hasAuthorship W3040606985A5015053512 @default.
• W3040606985 hasAuthorship W3040606985A5030967805 @default.
• W3040606985 hasAuthorship W3040606985A5049356493 @default.
• W3040606985 hasAuthorship W3040606985A5050248482 @default.
• W3040606985 hasAuthorship W3040606985A5058750595 @default.
• W3040606985 hasAuthorship W3040606985A5059452714 @default.
• W3040606985 hasAuthorship W3040606985A5087685316 @default.
• W3040606985 hasBestOaLocation W30406069851 @default.
• W3040606985 hasConcept C11783203 @default.
• W3040606985 hasConcept C118552586 @default.
• W3040606985 hasConcept C119857082 @default.
• W3040606985 hasConcept C126322002 @default.
• W3040606985 hasConcept C148483581 @default.
• W3040606985 hasConcept C151956035 @default.
• W3040606985 hasConcept C170964787 @default.
• W3040606985 hasConcept C194828623 @default.
• W3040606985 hasConcept C2777120189 @default.
• W3040606985 hasConcept C2778704086 @default.
• W3040606985 hasConcept C2779134260 @default.
• W3040606985 hasConcept C2780400711 @default.
• W3040606985 hasConcept C2780724011 @default.
• W3040606985 hasConcept C2780739214 @default.
• W3040606985 hasConcept C41008148 @default.
• W3040606985 hasConcept C500558357 @default.
• W3040606985 hasConcept C58471807 @default.
• W3040606985 hasConcept C71924100 @default.
• W3040606985 hasConceptScore W3040606985C11783203 @default.
• W3040606985 hasConceptScore W3040606985C118552586 @default.

• next