Matches in SemOpenAlex for { <https://semopenalex.org/work/W2033104789> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W2033104789 endingPage "231" @default.
- W2033104789 startingPage "222" @default.
- W2033104789 abstract "To verify whether symptoms reported by patients with uninvestigated dyspepsia might be helpful in either classifying functional from organic dyspepsia (1st experiment), or recognising which Helicobacter pylori infected patients may benefit from eradication therapy (2nd experiment).We compared the performance of artificial neural networks and linear discriminant analysis in two experiments on a database including socio-demographic features, past medical history, alarming symptoms, and symptoms at presentation of 860 patients with uninvestigated dyspepsia enrolled in a large observational multi-centre Italian study.In the 1st experiment, the best prediction for organic disease was given by the Sine Net model (specificity of 87.6% with 13 patients misclassified) and the best prediction for functional dyspepsia by the FF Bp model (sensitivity of 83.4% with 56 patients misclassified). The highest global accuracy of linear discriminant analysis was 65.1%, with 150 patients misclassified. In the 2nd experiment, the highest predictive performance was provided by the SelfDASn model: all infected patients who became symptom-free after successful eradicating treatment were correctly classified, whereas nine errors were made in forecasting patients who did not benefit from such a therapy. The highest global performance of linear discriminant analysis was 53.2%, with 37 patients misclassified.In patients with uninvestigated dyspepsia, artificial neural networks might have potential for categorising those affected by either organic or functional dyspepsia, as well as for identifying all Helicobacter pylori infected dyspeptic patients who will benefit from eradication." @default.
- W2033104789 created "2016-06-24" @default.
- W2033104789 creator A5001670233 @default.
- W2033104789 creator A5015267957 @default.
- W2033104789 creator A5021545379 @default.
- W2033104789 creator A5045740995 @default.
- W2033104789 creator A5048150723 @default.
- W2033104789 creator A5056404846 @default.
- W2033104789 creator A5071103909 @default.
- W2033104789 creator A5091031530 @default.
- W2033104789 date "2003-04-01" @default.
- W2033104789 modified "2023-09-26" @default.
- W2033104789 title "Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia" @default.
- W2033104789 cites W1983759636 @default.
- W2033104789 cites W1991238938 @default.
- W2033104789 cites W1992961864 @default.
- W2033104789 cites W2001282063 @default.
- W2033104789 cites W2022135315 @default.
- W2033104789 cites W2024482694 @default.
- W2033104789 cites W2039878344 @default.
- W2033104789 cites W2041928930 @default.
- W2033104789 cites W2056593417 @default.
- W2033104789 cites W2064037633 @default.
- W2033104789 cites W2065572354 @default.
- W2033104789 cites W2068859916 @default.
- W2033104789 cites W2073208108 @default.
- W2033104789 cites W2078178314 @default.
- W2033104789 cites W2091811990 @default.
- W2033104789 cites W2098247353 @default.
- W2033104789 cites W2108171381 @default.
- W2033104789 cites W2117098098 @default.
- W2033104789 cites W2131227729 @default.
- W2033104789 cites W2340283093 @default.
- W2033104789 cites W2396843678 @default.
- W2033104789 cites W4210813312 @default.
- W2033104789 cites W4237403611 @default.
- W2033104789 cites W4246719425 @default.
- W2033104789 cites W4249028950 @default.
- W2033104789 cites W4256012409 @default.
- W2033104789 doi "https://doi.org/10.1016/s1590-8658(03)00057-4" @default.
- W2033104789 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/12801032" @default.
- W2033104789 hasPublicationYear "2003" @default.
- W2033104789 type Work @default.
- W2033104789 sameAs 2033104789 @default.
- W2033104789 citedByCount "23" @default.
- W2033104789 countsByYear W20331047892013 @default.
- W2033104789 countsByYear W20331047892015 @default.
- W2033104789 countsByYear W20331047892020 @default.
- W2033104789 countsByYear W20331047892022 @default.
- W2033104789 crossrefType "journal-article" @default.
- W2033104789 hasAuthorship W2033104789A5001670233 @default.
- W2033104789 hasAuthorship W2033104789A5015267957 @default.
- W2033104789 hasAuthorship W2033104789A5021545379 @default.
- W2033104789 hasAuthorship W2033104789A5045740995 @default.
- W2033104789 hasAuthorship W2033104789A5048150723 @default.
- W2033104789 hasAuthorship W2033104789A5056404846 @default.
- W2033104789 hasAuthorship W2033104789A5071103909 @default.
- W2033104789 hasAuthorship W2033104789A5091031530 @default.
- W2033104789 hasConcept C126322002 @default.
- W2033104789 hasConcept C154945302 @default.
- W2033104789 hasConcept C23131810 @default.
- W2033104789 hasConcept C2776409635 @default.
- W2033104789 hasConcept C2779134260 @default.
- W2033104789 hasConcept C2908868296 @default.
- W2033104789 hasConcept C41008148 @default.
- W2033104789 hasConcept C69738355 @default.
- W2033104789 hasConcept C71924100 @default.
- W2033104789 hasConcept C90924648 @default.
- W2033104789 hasConceptScore W2033104789C126322002 @default.
- W2033104789 hasConceptScore W2033104789C154945302 @default.
- W2033104789 hasConceptScore W2033104789C23131810 @default.
- W2033104789 hasConceptScore W2033104789C2776409635 @default.
- W2033104789 hasConceptScore W2033104789C2779134260 @default.
- W2033104789 hasConceptScore W2033104789C2908868296 @default.
- W2033104789 hasConceptScore W2033104789C41008148 @default.
- W2033104789 hasConceptScore W2033104789C69738355 @default.
- W2033104789 hasConceptScore W2033104789C71924100 @default.
- W2033104789 hasConceptScore W2033104789C90924648 @default.
- W2033104789 hasIssue "4" @default.
- W2033104789 hasLocation W20331047891 @default.
- W2033104789 hasLocation W20331047892 @default.
- W2033104789 hasOpenAccess W2033104789 @default.
- W2033104789 hasPrimaryLocation W20331047891 @default.
- W2033104789 hasRelatedWork W2018865736 @default.
- W2033104789 hasRelatedWork W2137046107 @default.
- W2033104789 hasRelatedWork W2169584830 @default.
- W2033104789 hasRelatedWork W2353749324 @default.
- W2033104789 hasRelatedWork W2363023711 @default.
- W2033104789 hasRelatedWork W2371051333 @default.
- W2033104789 hasRelatedWork W2383043324 @default.
- W2033104789 hasRelatedWork W2389411873 @default.
- W2033104789 hasRelatedWork W3173891479 @default.
- W2033104789 hasRelatedWork W2239945818 @default.
- W2033104789 hasVolume "35" @default.
- W2033104789 isParatext "false" @default.
- W2033104789 isRetracted "false" @default.
- W2033104789 magId "2033104789" @default.
- W2033104789 workType "article" @default.