FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1486210018> ?p ?o ?g. }

• W1486210018 endingPage "R150" @default.
• W1486210018 startingPage "R150" @default.
• W1486210018 abstract "Neural networks are new methodological tools based on nonlinear models. They appear to be better at prediction and classification in biological systems than do traditional strategies such as logistic regression. This paper provides a practical example that contrasts both approaches within the setting of suspected sepsis in the emergency room.The study population comprised patients with suspected bacterial infection as their main diagnosis for admission to the emergency room at two University-based hospitals. Mortality within the first 28 days from admission was predicted using logistic regression with the following variables: age, immunosuppressive systemic disease, general systemic disease, Shock Index, temperature, respiratory rate, Glasgow Coma Scale score, leucocyte counts, platelet counts and creatinine. Also, with the same input and output variables, a probabilistic neural network was trained with an adaptive genetic algorithm. The network had three neurone layers: 10 neurones in the input layer, 368 in the hidden layer and two in the output layer. Calibration was measured using the Hosmer-Lemeshow goodness-of-fit test and discrimination was determined using receiver operating characteristic curves.A total of 533 patients were recruited and overall 28-day mortality was 19%. The factors chosen by logistic regression (with their score in parentheses) were as follows: immunosuppressive systemic disease or general systemic disease (2), respiratory rate 24-33 breaths/min (1), respiratory rate > or = 34 breaths/min (3), Glasgow Come Scale score < or = 12 (3), Shock Index > or = 1.5 (2) and temperature < 38 degrees C (2). The network included all variables and there were no significant differences in predictive ability between the approaches. The areas under the receiver operating characteristic curves were 0.7517 and 0.8782 for the logistic model and the neural network, respectively (P = 0.037).A predictive model would be an extremely useful tool in the setting of suspected sepsis in the emergency room. It could serve both as a guideline in medical decision-making and as a simple way to select or stratify patients in clinical research. Our proposed model and the specific development method -- either logistic regression or neural networks -- must be evaluated and validated in an independent population." @default.
• W1486210018 created "2016-06-24" @default.
• W1486210018 creator A5012098476 @default.
• W1486210018 creator A5044355109 @default.
• W1486210018 creator A5080374744 @default.
• W1486210018 creator A5080507179 @default.
• W1486210018 date "2005-01-01" @default.
• W1486210018 modified "2023-09-27" @default.
• W1486210018 cites W1998077350 @default.
• W1486210018 cites W2008384254 @default.
• W1486210018 cites W2008448636 @default.
• W1486210018 cites W2054921873 @default.
• W1486210018 cites W2064271598 @default.
• W1486210018 cites W2080696064 @default.
• W1486210018 cites W2089463633 @default.
• W1486210018 cites W2092762545 @default.
• W1486210018 cites W2092808594 @default.
• W1486210018 cites W2094104004 @default.
• W1486210018 cites W2121394390 @default.
• W1486210018 cites W2123151246 @default.
• W1486210018 cites W2145577370 @default.
• W1486210018 cites W2157825442 @default.
• W1486210018 cites W4301177039 @default.
• W1486210018 doi "https://doi.org/10.1186/cc3054" @default.
• W1486210018 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1175932" @default.
• W1486210018 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15774048" @default.
• W1486210018 hasPublicationYear "2005" @default.
• W1486210018 type Work @default.
• W1486210018 sameAs 1486210018 @default.
• W1486210018 citedByCount "105" @default.
• W1486210018 countsByYear W14862100182012 @default.
• W1486210018 countsByYear W14862100182013 @default.
• W1486210018 countsByYear W14862100182014 @default.
• W1486210018 countsByYear W14862100182015 @default.
• W1486210018 countsByYear W14862100182016 @default.
• W1486210018 countsByYear W14862100182017 @default.
• W1486210018 countsByYear W14862100182018 @default.
• W1486210018 countsByYear W14862100182019 @default.
• W1486210018 countsByYear W14862100182020 @default.
• W1486210018 countsByYear W14862100182021 @default.
• W1486210018 countsByYear W14862100182022 @default.
• W1486210018 countsByYear W14862100182023 @default.
• W1486210018 crossrefType "journal-article" @default.
• W1486210018 hasAuthorship W1486210018A5012098476 @default.
• W1486210018 hasAuthorship W1486210018A5044355109 @default.
• W1486210018 hasAuthorship W1486210018A5080374744 @default.
• W1486210018 hasAuthorship W1486210018A5080507179 @default.
• W1486210018 hasBestOaLocation W14862100181 @default.
• W1486210018 hasConcept C105795698 @default.
• W1486210018 hasConcept C126322002 @default.
• W1486210018 hasConcept C141071460 @default.
• W1486210018 hasConcept C151956035 @default.
• W1486210018 hasConcept C17624336 @default.
• W1486210018 hasConcept C194828623 @default.
• W1486210018 hasConcept C2908647359 @default.
• W1486210018 hasConcept C33923547 @default.
• W1486210018 hasConcept C43346845 @default.
• W1486210018 hasConcept C58471807 @default.
• W1486210018 hasConcept C71924100 @default.
• W1486210018 hasConcept C99454951 @default.
• W1486210018 hasConceptScore W1486210018C105795698 @default.
• W1486210018 hasConceptScore W1486210018C126322002 @default.
• W1486210018 hasConceptScore W1486210018C141071460 @default.
• W1486210018 hasConceptScore W1486210018C151956035 @default.
• W1486210018 hasConceptScore W1486210018C17624336 @default.
• W1486210018 hasConceptScore W1486210018C194828623 @default.
• W1486210018 hasConceptScore W1486210018C2908647359 @default.
• W1486210018 hasConceptScore W1486210018C33923547 @default.
• W1486210018 hasConceptScore W1486210018C43346845 @default.
• W1486210018 hasConceptScore W1486210018C58471807 @default.
• W1486210018 hasConceptScore W1486210018C71924100 @default.
• W1486210018 hasConceptScore W1486210018C99454951 @default.
• W1486210018 hasIssue "2" @default.
• W1486210018 hasLocation W14862100181 @default.
• W1486210018 hasLocation W14862100182 @default.
• W1486210018 hasLocation W14862100183 @default.
• W1486210018 hasLocation W14862100184 @default.
• W1486210018 hasOpenAccess W1486210018 @default.
• W1486210018 hasPrimaryLocation W14862100181 @default.
• W1486210018 hasRelatedWork W1999820593 @default.
• W1486210018 hasRelatedWork W2012775041 @default.
• W1486210018 hasRelatedWork W2027684363 @default.
• W1486210018 hasRelatedWork W2157758999 @default.
• W1486210018 hasRelatedWork W2394284613 @default.
• W1486210018 hasRelatedWork W2981095150 @default.
• W1486210018 hasRelatedWork W3008180295 @default.
• W1486210018 hasRelatedWork W3029555405 @default.
• W1486210018 hasRelatedWork W3149182572 @default.
• W1486210018 hasRelatedWork W3204711385 @default.
• W1486210018 hasVolume "9" @default.
• W1486210018 isParatext "false" @default.
• W1486210018 isRetracted "false" @default.
• W1486210018 magId "1486210018" @default.
• W1486210018 workType "article" @default.