FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2742421437> ?p ?o ?g. }

• W2742421437 abstract "Background Different machine learning methods have been used to develop predictive models of high quality and precision [1]. Among them, Random Survival Forests (RSF) has been proposed as an alternative to traditional survival models [2], being able to overcome most of the limitation of traditional survival techniques, such as Cox proportional hazards models. Objectives Our objective was to develop and internally validate a predictive model for rheumatoid arthritis (RA) mortality using Random Survival Forests (RSF). Methods Retrospective longitudinal study involving 1,461 patients diagnosed with RA between January 1994 and August 2011, and followed at the outpatient clinic of the Rheumatology Department of the Hospital Clinico San Carlos (Madrid, Spain) until death or September 2013. Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. RSF models were developed, based on 1,000 trees. 100 iterations of each model were performed to measure the mean and standard deviation (SD) of the predictive error and the integrated Brier score (IBS). Missing values were imputed using the function implemented by the randomForestSRC package [3]. The predictive capacity of the variables was assessed using the “variable importance” (VIMP). Two models were constructed using the log-rank (M LG ) or log-rank score (M LGS ) splitting rules. The model with the lowest prediction error was selected. Next, those variables with negative VIMP were excluded and a final model developed. Results 148 patients died (10.1%). M LG showed the lowest prediction error. All variables exhibited a positive VIMP. Final model showed a mean (SD) prediction error and IBS of 0.187 (0.002) and 0.150 (0.003) respectively. The most important predictor variables were age at diagnosis, median erythrocyte sedimentation rate and number of hospital admissions in the first 2 years after RA diagnosis. Conclusions We developed an accurate and precise model for RA mortality using RSF. Age and disease activity showed the highest influence in mortality. References Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards. Crit Care Med. 2016;44: 368–74. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2: 841–860. Ishwaran H, Kogalur U. Random Forests for Survival, Regression and Classification (RF-SRC) [Internet]. 2016 [cited 15 Dec 2016]. Available: https://cran.r-project.org/package=randomForestSRC. Disclosure of Interest None declared" @default.
• W2742421437 created "2017-08-17" @default.
• W2742421437 creator A5001057149 @default.
• W2742421437 creator A5014550963 @default.
• W2742421437 creator A5030899790 @default.
• W2742421437 creator A5042249694 @default.
• W2742421437 creator A5064534346 @default.
• W2742421437 creator A5085714763 @default.
• W2742421437 creator A5085721515 @default.
• W2742421437 date "2017-06-01" @default.
• W2742421437 modified "2023-09-26" @default.
• W2742421437 title "FRI0107 Development of a predictive model for rheumatoid arthritis mortality using random survival forest" @default.
• W2742421437 doi "https://doi.org/10.1136/annrheumdis-2017-eular.6139" @default.
• W2742421437 hasPublicationYear "2017" @default.
• W2742421437 type Work @default.
• W2742421437 sameAs 2742421437 @default.
• W2742421437 citedByCount "0" @default.
• W2742421437 crossrefType "proceedings-article" @default.
• W2742421437 hasAuthorship W2742421437A5001057149 @default.
• W2742421437 hasAuthorship W2742421437A5014550963 @default.
• W2742421437 hasAuthorship W2742421437A5030899790 @default.
• W2742421437 hasAuthorship W2742421437A5042249694 @default.
• W2742421437 hasAuthorship W2742421437A5064534346 @default.
• W2742421437 hasAuthorship W2742421437A5085714763 @default.
• W2742421437 hasAuthorship W2742421437A5085721515 @default.
• W2742421437 hasBestOaLocation W27424214371 @default.
• W2742421437 hasConcept C10515644 @default.
• W2742421437 hasConcept C105795698 @default.
• W2742421437 hasConcept C119857082 @default.
• W2742421437 hasConcept C126322002 @default.
• W2742421437 hasConcept C169258074 @default.
• W2742421437 hasConcept C18747219 @default.
• W2742421437 hasConcept C2777575956 @default.
• W2742421437 hasConcept C33923547 @default.
• W2742421437 hasConcept C35405484 @default.
• W2742421437 hasConcept C41008148 @default.
• W2742421437 hasConcept C50382708 @default.
• W2742421437 hasConcept C71924100 @default.
• W2742421437 hasConceptScore W2742421437C10515644 @default.
• W2742421437 hasConceptScore W2742421437C105795698 @default.
• W2742421437 hasConceptScore W2742421437C119857082 @default.
• W2742421437 hasConceptScore W2742421437C126322002 @default.
• W2742421437 hasConceptScore W2742421437C169258074 @default.
• W2742421437 hasConceptScore W2742421437C18747219 @default.
• W2742421437 hasConceptScore W2742421437C2777575956 @default.
• W2742421437 hasConceptScore W2742421437C33923547 @default.
• W2742421437 hasConceptScore W2742421437C35405484 @default.
• W2742421437 hasConceptScore W2742421437C41008148 @default.
• W2742421437 hasConceptScore W2742421437C50382708 @default.
• W2742421437 hasConceptScore W2742421437C71924100 @default.
• W2742421437 hasLocation W27424214371 @default.
• W2742421437 hasOpenAccess W2742421437 @default.
• W2742421437 hasPrimaryLocation W27424214371 @default.
• W2742421437 hasRelatedWork W1811483620 @default.
• W2742421437 hasRelatedWork W1977231265 @default.
• W2742421437 hasRelatedWork W2094493089 @default.
• W2742421437 hasRelatedWork W2322390600 @default.
• W2742421437 hasRelatedWork W2466157359 @default.
• W2742421437 hasRelatedWork W2747987134 @default.
• W2742421437 hasRelatedWork W2764175852 @default.
• W2742421437 hasRelatedWork W3014109059 @default.
• W2742421437 hasRelatedWork W3017053225 @default.
• W2742421437 hasRelatedWork W3025819728 @default.
• W2742421437 hasRelatedWork W3026984459 @default.
• W2742421437 hasRelatedWork W3030944503 @default.
• W2742421437 hasRelatedWork W3048887085 @default.
• W2742421437 hasRelatedWork W3097224450 @default.
• W2742421437 hasRelatedWork W3106377951 @default.
• W2742421437 hasRelatedWork W3115995369 @default.
• W2742421437 hasRelatedWork W3171994353 @default.
• W2742421437 hasRelatedWork W3206611319 @default.
• W2742421437 hasRelatedWork W3206761101 @default.
• W2742421437 hasRelatedWork W2466840382 @default.
• W2742421437 isParatext "false" @default.
• W2742421437 isRetracted "false" @default.
• W2742421437 magId "2742421437" @default.
• W2742421437 workType "article" @default.