FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3100278850> ?p ?o ?g. }

• W3100278850 abstract "Abstract Stroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke." @default.
• W3100278850 created "2020-11-23" @default.
• W3100278850 creator A5018342706 @default.
• W3100278850 creator A5019589615 @default.
• W3100278850 creator A5023390430 @default.
• W3100278850 creator A5042052380 @default.
• W3100278850 creator A5044617873 @default.
• W3100278850 creator A5063120350 @default.
• W3100278850 creator A5070033950 @default.
• W3100278850 creator A5082714564 @default.
• W3100278850 creator A5082849561 @default.
• W3100278850 date "2020-11-18" @default.
• W3100278850 modified "2023-10-06" @default.
• W3100278850 title "Machine learning to predict mortality after rehabilitation among patients with severe stroke" @default.
• W3100278850 cites W1970530475 @default.
• W3100278850 cites W1975073117 @default.
• W3100278850 cites W1988790447 @default.
• W3100278850 cites W1998617203 @default.
• W3100278850 cites W2014757808 @default.
• W3100278850 cites W2022964202 @default.
• W3100278850 cites W2030472502 @default.
• W3100278850 cites W2057310032 @default.
• W3100278850 cites W2058294781 @default.
• W3100278850 cites W2067667819 @default.
• W3100278850 cites W2070493638 @default.
• W3100278850 cites W2070558888 @default.
• W3100278850 cites W2107138773 @default.
• W3100278850 cites W2114428908 @default.
• W3100278850 cites W2124675680 @default.
• W3100278850 cites W2125065061 @default.
• W3100278850 cites W2140713107 @default.
• W3100278850 cites W2148143831 @default.
• W3100278850 cites W2149992821 @default.
• W3100278850 cites W2150780222 @default.
• W3100278850 cites W2164416219 @default.
• W3100278850 cites W2171880598 @default.
• W3100278850 cites W2177870565 @default.
• W3100278850 cites W2187039239 @default.
• W3100278850 cites W2330219538 @default.
• W3100278850 cites W2395649774 @default.
• W3100278850 cites W2551155708 @default.
• W3100278850 cites W2602013531 @default.
• W3100278850 cites W2607112991 @default.
• W3100278850 cites W2743269518 @default.
• W3100278850 cites W2747242955 @default.
• W3100278850 cites W2748317569 @default.
• W3100278850 cites W2754906766 @default.
• W3100278850 cites W2758815285 @default.
• W3100278850 cites W2769119430 @default.
• W3100278850 cites W2773904775 @default.
• W3100278850 cites W2774832747 @default.
• W3100278850 cites W2784094750 @default.
• W3100278850 cites W2789744550 @default.
• W3100278850 cites W2803306216 @default.
• W3100278850 cites W2804929967 @default.
• W3100278850 cites W2884590110 @default.
• W3100278850 cites W2908354210 @default.
• W3100278850 cites W2911964244 @default.
• W3100278850 cites W2921112006 @default.
• W3100278850 cites W2967844572 @default.
• W3100278850 cites W2968695270 @default.
• W3100278850 cites W2985220169 @default.
• W3100278850 cites W2995774252 @default.
• W3100278850 cites W2997681568 @default.
• W3100278850 cites W2999645440 @default.
• W3100278850 cites W3007416753 @default.
• W3100278850 cites W3008442195 @default.
• W3100278850 cites W3010733248 @default.
• W3100278850 cites W3011403448 @default.
• W3100278850 cites W3011855383 @default.
• W3100278850 cites W3017085492 @default.
• W3100278850 cites W3025390501 @default.
• W3100278850 cites W3026428279 @default.
• W3100278850 cites W3026434751 @default.
• W3100278850 cites W3064554225 @default.
• W3100278850 doi "https://doi.org/10.1038/s41598-020-77243-3" @default.
• W3100278850 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7674405" @default.
• W3100278850 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33208913" @default.
• W3100278850 hasPublicationYear "2020" @default.
• W3100278850 type Work @default.
• W3100278850 sameAs 3100278850 @default.
• W3100278850 citedByCount "46" @default.
• W3100278850 countsByYear W31002788502021 @default.
• W3100278850 countsByYear W31002788502022 @default.
• W3100278850 countsByYear W31002788502023 @default.
• W3100278850 crossrefType "journal-article" @default.
• W3100278850 hasAuthorship W3100278850A5018342706 @default.
• W3100278850 hasAuthorship W3100278850A5019589615 @default.
• W3100278850 hasAuthorship W3100278850A5023390430 @default.
• W3100278850 hasAuthorship W3100278850A5042052380 @default.
• W3100278850 hasAuthorship W3100278850A5044617873 @default.
• W3100278850 hasAuthorship W3100278850A5063120350 @default.
• W3100278850 hasAuthorship W3100278850A5070033950 @default.
• W3100278850 hasAuthorship W3100278850A5082714564 @default.
• W3100278850 hasAuthorship W3100278850A5082849561 @default.
• W3100278850 hasBestOaLocation W31002788501 @default.
• W3100278850 hasConcept C105795698 @default.
• W3100278850 hasConcept C119857082 @default.
• W3100278850 hasConcept C127413603 @default.
• W3100278850 hasConcept C151956035 @default.

• next