FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3105811588> ?p ?o ?g. }

• W3105811588 abstract "Abstract Background Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2. Method Between March 1 and April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: (1) a Cox regression model and (2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration. Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI) 73.8–91.1 and 90.0%, 95% CI 81.2–95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI 91.1–94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI 85.7–88.2), p = 0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. Conclusion We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level." @default.
• W3105811588 created "2020-11-23" @default.
• W3105811588 creator A5000313377 @default.
• W3105811588 creator A5007840115 @default.
• W3105811588 creator A5011271764 @default.
• W3105811588 creator A5050198943 @default.
• W3105811588 creator A5060261472 @default.
• W3105811588 creator A5062931451 @default.
• W3105811588 creator A5065784852 @default.
• W3105811588 creator A5077338630 @default.
• W3105811588 date "2020-11-19" @default.
• W3105811588 modified "2023-10-14" @default.
• W3105811588 title "Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes" @default.
• W3105811588 cites W2012289572 @default.
• W3105811588 cites W2026042561 @default.
• W3105811588 cites W2042571564 @default.
• W3105811588 cites W2045257928 @default.
• W3105811588 cites W2078271269 @default.
• W3105811588 cites W2328176404 @default.
• W3105811588 cites W2913997948 @default.
• W3105811588 cites W2951553997 @default.
• W3105811588 cites W2963012631 @default.
• W3105811588 cites W2964696298 @default.
• W3105811588 cites W2965640442 @default.
• W3105811588 cites W3007416325 @default.
• W3105811588 cites W3009885589 @default.
• W3105811588 cites W3010667412 @default.
• W3105811588 cites W3011414603 @default.
• W3105811588 cites W3011716991 @default.
• W3105811588 cites W3014028105 @default.
• W3105811588 cites W3014289208 @default.
• W3105811588 cites W3014524604 @default.
• W3105811588 cites W3014604938 @default.
• W3105811588 cites W3017022286 @default.
• W3105811588 cites W3026764413 @default.
• W3105811588 cites W3031632781 @default.
• W3105811588 cites W3034560014 @default.
• W3105811588 cites W3037794822 @default.
• W3105811588 cites W3044899979 @default.
• W3105811588 cites W3049701554 @default.
• W3105811588 cites W3125804999 @default.
• W3105811588 cites W4210642183 @default.
• W3105811588 doi "https://doi.org/10.1186/s12911-020-01316-6" @default.
• W3105811588 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7676403" @default.
• W3105811588 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33213435" @default.
• W3105811588 hasPublicationYear "2020" @default.
• W3105811588 type Work @default.
• W3105811588 sameAs 3105811588 @default.
• W3105811588 citedByCount "26" @default.
• W3105811588 countsByYear W31058115882021 @default.
• W3105811588 countsByYear W31058115882022 @default.
• W3105811588 countsByYear W31058115882023 @default.
• W3105811588 crossrefType "journal-article" @default.
• W3105811588 hasAuthorship W3105811588A5000313377 @default.
• W3105811588 hasAuthorship W3105811588A5007840115 @default.
• W3105811588 hasAuthorship W3105811588A5011271764 @default.
• W3105811588 hasAuthorship W3105811588A5050198943 @default.
• W3105811588 hasAuthorship W3105811588A5060261472 @default.
• W3105811588 hasAuthorship W3105811588A5062931451 @default.
• W3105811588 hasAuthorship W3105811588A5065784852 @default.
• W3105811588 hasAuthorship W3105811588A5077338630 @default.
• W3105811588 hasBestOaLocation W31058115881 @default.
• W3105811588 hasConcept C105795698 @default.
• W3105811588 hasConcept C119857082 @default.
• W3105811588 hasConcept C126322002 @default.
• W3105811588 hasConcept C138816342 @default.
• W3105811588 hasConcept C142724271 @default.
• W3105811588 hasConcept C145642194 @default.
• W3105811588 hasConcept C151956035 @default.
• W3105811588 hasConcept C152877465 @default.
• W3105811588 hasConcept C154945302 @default.
• W3105811588 hasConcept C194828623 @default.
• W3105811588 hasConcept C33923547 @default.
• W3105811588 hasConcept C35405484 @default.
• W3105811588 hasConcept C41008148 @default.
• W3105811588 hasConcept C44249647 @default.
• W3105811588 hasConcept C45804977 @default.
• W3105811588 hasConcept C50382708 @default.
• W3105811588 hasConcept C50644808 @default.
• W3105811588 hasConcept C58471807 @default.
• W3105811588 hasConcept C71924100 @default.
• W3105811588 hasConcept C83546350 @default.
• W3105811588 hasConceptScore W3105811588C105795698 @default.
• W3105811588 hasConceptScore W3105811588C119857082 @default.
• W3105811588 hasConceptScore W3105811588C126322002 @default.
• W3105811588 hasConceptScore W3105811588C138816342 @default.
• W3105811588 hasConceptScore W3105811588C142724271 @default.
• W3105811588 hasConceptScore W3105811588C145642194 @default.
• W3105811588 hasConceptScore W3105811588C151956035 @default.
• W3105811588 hasConceptScore W3105811588C152877465 @default.
• W3105811588 hasConceptScore W3105811588C154945302 @default.
• W3105811588 hasConceptScore W3105811588C194828623 @default.
• W3105811588 hasConceptScore W3105811588C33923547 @default.
• W3105811588 hasConceptScore W3105811588C35405484 @default.
• W3105811588 hasConceptScore W3105811588C41008148 @default.
• W3105811588 hasConceptScore W3105811588C44249647 @default.
• W3105811588 hasConceptScore W3105811588C45804977 @default.
• W3105811588 hasConceptScore W3105811588C50382708 @default.
• W3105811588 hasConceptScore W3105811588C50644808 @default.
• W3105811588 hasConceptScore W3105811588C58471807 @default.

• next