FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4316038429> ?p ?o ?g. }

• W4316038429 endingPage "7" @default.
• W4316038429 startingPage "1" @default.
• W4316038429 abstract "Risk stratification for three-vessel coronary artery disease (3VD) remains an important clinical challenge. In this study, we utilized machine learning (ML), which can address the limitations of traditional regression-based models, to develop a novel model to assess mortality risk in patients with 3VD.This study was based on a prospective cohort of 8943 participants with 3VD consecutively enrolled between 2004 and 2011. An ML-derived random forest model was trained and tested to predict 4-year mortality. The predictability of the model was compared with that of an established model, the Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery score II (SSII), among 3VD patients undergoing percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), and medical therapy (MT) alone.The all-cause mortality was 7.5% (667 patients) over the 4-year follow-up period. The correlation-based feature selection algorithm selected 18 of the 94 features to develop the ML model. In the testing dataset, the ML-derived model achieved an area under the curve of 0.81 for 4-year mortality prediction. Its predictability was significantly better than that of the SSII among patients undergoing PCI (0.80 vs. 0.70, p < 0.001) or CABG (0.80 vs. 0.67, p < 0.001). The model also outperformed the SSII in patients receiving MT alone (ML: 0.75 vs. SSII for PCI: 0.70 or SSII for CABG: 0.66, p < 0.001).This ML-based approach exhibited better performance in risk stratification for 3VD compared with the conventional method. Further validation studies are needed to confirm these findings." @default.
• W4316038429 created "2023-01-14" @default.
• W4316038429 creator A5003019918 @default.
• W4316038429 creator A5010475253 @default.
• W4316038429 creator A5017227883 @default.
• W4316038429 creator A5018869124 @default.
• W4316038429 creator A5020270077 @default.
• W4316038429 creator A5020994307 @default.
• W4316038429 creator A5023391846 @default.
• W4316038429 creator A5024401768 @default.
• W4316038429 creator A5038547888 @default.
• W4316038429 creator A5040682452 @default.
• W4316038429 creator A5045349004 @default.
• W4316038429 creator A5046454314 @default.
• W4316038429 creator A5047455796 @default.
• W4316038429 creator A5052150406 @default.
• W4316038429 creator A5053179595 @default.
• W4316038429 creator A5056125607 @default.
• W4316038429 creator A5061528278 @default.
• W4316038429 creator A5067328884 @default.
• W4316038429 creator A5079141255 @default.
• W4316038429 date "2023-02-01" @default.
• W4316038429 modified "2023-09-25" @default.
• W4316038429 title "Machine learning improves mortality prediction in three-vessel disease" @default.
• W4316038429 cites W140064290 @default.
• W4316038429 cites W1995870068 @default.
• W4316038429 cites W2133990480 @default.
• W4316038429 cites W2140991303 @default.
• W4316038429 cites W2141686284 @default.
• W4316038429 cites W2158134519 @default.
• W4316038429 cites W2328176404 @default.
• W4316038429 cites W2506073361 @default.
• W4316038429 cites W2605396475 @default.
• W4316038429 cites W2615022822 @default.
• W4316038429 cites W2616651817 @default.
• W4316038429 cites W2778596258 @default.
• W4316038429 cites W2911964244 @default.
• W4316038429 cites W2913702106 @default.
• W4316038429 cites W2916912835 @default.
• W4316038429 cites W2954860393 @default.
• W4316038429 cites W2995889897 @default.
• W4316038429 cites W2997822868 @default.
• W4316038429 cites W3008117014 @default.
• W4316038429 doi "https://doi.org/10.1016/j.atherosclerosis.2023.01.003" @default.
• W4316038429 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36706681" @default.
• W4316038429 hasPublicationYear "2023" @default.
• W4316038429 type Work @default.
• W4316038429 citedByCount "0" @default.
• W4316038429 crossrefType "journal-article" @default.
• W4316038429 hasAuthorship W4316038429A5003019918 @default.
• W4316038429 hasAuthorship W4316038429A5010475253 @default.
• W4316038429 hasAuthorship W4316038429A5017227883 @default.
• W4316038429 hasAuthorship W4316038429A5018869124 @default.
• W4316038429 hasAuthorship W4316038429A5020270077 @default.
• W4316038429 hasAuthorship W4316038429A5020994307 @default.
• W4316038429 hasAuthorship W4316038429A5023391846 @default.
• W4316038429 hasAuthorship W4316038429A5024401768 @default.
• W4316038429 hasAuthorship W4316038429A5038547888 @default.
• W4316038429 hasAuthorship W4316038429A5040682452 @default.
• W4316038429 hasAuthorship W4316038429A5045349004 @default.
• W4316038429 hasAuthorship W4316038429A5046454314 @default.
• W4316038429 hasAuthorship W4316038429A5047455796 @default.
• W4316038429 hasAuthorship W4316038429A5052150406 @default.
• W4316038429 hasAuthorship W4316038429A5053179595 @default.
• W4316038429 hasAuthorship W4316038429A5056125607 @default.
• W4316038429 hasAuthorship W4316038429A5061528278 @default.
• W4316038429 hasAuthorship W4316038429A5067328884 @default.
• W4316038429 hasAuthorship W4316038429A5079141255 @default.
• W4316038429 hasConcept C105795698 @default.
• W4316038429 hasConcept C126322002 @default.
• W4316038429 hasConcept C141071460 @default.
• W4316038429 hasConcept C164705383 @default.
• W4316038429 hasConcept C188816634 @default.
• W4316038429 hasConcept C197640229 @default.
• W4316038429 hasConcept C2778213512 @default.
• W4316038429 hasConcept C2780400711 @default.
• W4316038429 hasConcept C33923547 @default.
• W4316038429 hasConcept C45393284 @default.
• W4316038429 hasConcept C500558357 @default.
• W4316038429 hasConcept C71924100 @default.
• W4316038429 hasConcept C72563966 @default.
• W4316038429 hasConceptScore W4316038429C105795698 @default.
• W4316038429 hasConceptScore W4316038429C126322002 @default.
• W4316038429 hasConceptScore W4316038429C141071460 @default.
• W4316038429 hasConceptScore W4316038429C164705383 @default.
• W4316038429 hasConceptScore W4316038429C188816634 @default.
• W4316038429 hasConceptScore W4316038429C197640229 @default.
• W4316038429 hasConceptScore W4316038429C2778213512 @default.
• W4316038429 hasConceptScore W4316038429C2780400711 @default.
• W4316038429 hasConceptScore W4316038429C33923547 @default.
• W4316038429 hasConceptScore W4316038429C45393284 @default.
• W4316038429 hasConceptScore W4316038429C500558357 @default.
• W4316038429 hasConceptScore W4316038429C71924100 @default.
• W4316038429 hasConceptScore W4316038429C72563966 @default.
• W4316038429 hasFunder F4320323137 @default.
• W4316038429 hasFunder F4320325902 @default.
• W4316038429 hasLocation W43160384291 @default.
• W4316038429 hasLocation W43160384292 @default.

• next