Matches in SemOpenAlex for { <https://semopenalex.org/work/W3121614855> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W3121614855 endingPage "872" @default.
- W3121614855 startingPage "864" @default.
- W3121614855 abstract "Summary Background The risk of gastric cancer after Helicobacter pylori ( H. pylori ) eradication remains unknown. Aim To evaluate the performances of seven different machine learning models in predicting gastric cancer risk after H. pylori eradication. Methods We identified H. pylori ‐infected patients who had received clarithromycin‐based triple therapy between 2003 and 2014 in Hong Kong. Patients were divided into training (n = 64 238) and validation sets (n = 25 330), according to period of eradication therapy. The data were used to construct seven machine learning models to predict risk of gastric cancer development within 5 years after H. pylori eradication. A total of 26 clinical variables were input into these models. The performances were measured by the area under receiver operating characteristic curve (AUC) analysis. Results During a mean follow‐up of 4.7 years, 0.21% of H. pylori ‐eradicated patients developed gastric cancer. Of the seven machine learning models, extreme gradient boosting (XGBoost) had the best performance in predicting cancer development (AUC 0.97, 95%CI 0.96‐0.98), and was superior to conventional logistic regression (AUC 0.90, 95% CI 0.84‐0.92). With the XGBoost model, the number of patients considered at high risk of gastric cancer was 6.6%, with miss rate of 1.9%. Patient age, presence of intestinal metaplasia, and gastric ulcer were the heavily weighted factors used by the XGBoost. Conclusion Based on simple baseline patient information, machine learning model can accurately predict the risk of post‐eradication gastric cancer. This model could substantially reduce the number of patients who require endoscopic surveillance." @default.
- W3121614855 created "2021-02-01" @default.
- W3121614855 creator A5046480357 @default.
- W3121614855 creator A5048726723 @default.
- W3121614855 creator A5063047136 @default.
- W3121614855 creator A5078047862 @default.
- W3121614855 creator A5083454533 @default.
- W3121614855 date "2021-01-24" @default.
- W3121614855 modified "2023-10-11" @default.
- W3121614855 title "Applications of machine learning models in the prediction of gastric cancer risk in patients after <i>Helicobacter pylori</i> eradication" @default.
- W3121614855 cites W1829056251 @default.
- W3121614855 cites W2070493638 @default.
- W3121614855 cites W2082580119 @default.
- W3121614855 cites W2085043416 @default.
- W3121614855 cites W2252730161 @default.
- W3121614855 cites W2254794399 @default.
- W3121614855 cites W2580621725 @default.
- W3121614855 cites W2766436834 @default.
- W3121614855 cites W2768768110 @default.
- W3121614855 cites W2793549397 @default.
- W3121614855 cites W2800194289 @default.
- W3121614855 cites W2897774900 @default.
- W3121614855 cites W2921353582 @default.
- W3121614855 cites W2927994835 @default.
- W3121614855 cites W2928498900 @default.
- W3121614855 cites W2958490118 @default.
- W3121614855 cites W2976369463 @default.
- W3121614855 cites W2990272740 @default.
- W3121614855 cites W299260745 @default.
- W3121614855 cites W2994498656 @default.
- W3121614855 cites W3013330736 @default.
- W3121614855 doi "https://doi.org/10.1111/apt.16272" @default.
- W3121614855 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33486805" @default.
- W3121614855 hasPublicationYear "2021" @default.
- W3121614855 type Work @default.
- W3121614855 sameAs 3121614855 @default.
- W3121614855 citedByCount "20" @default.
- W3121614855 countsByYear W31216148552021 @default.
- W3121614855 countsByYear W31216148552022 @default.
- W3121614855 countsByYear W31216148552023 @default.
- W3121614855 crossrefType "journal-article" @default.
- W3121614855 hasAuthorship W3121614855A5046480357 @default.
- W3121614855 hasAuthorship W3121614855A5048726723 @default.
- W3121614855 hasAuthorship W3121614855A5063047136 @default.
- W3121614855 hasAuthorship W3121614855A5078047862 @default.
- W3121614855 hasAuthorship W3121614855A5083454533 @default.
- W3121614855 hasConcept C121608353 @default.
- W3121614855 hasConcept C126322002 @default.
- W3121614855 hasConcept C151956035 @default.
- W3121614855 hasConcept C2776409635 @default.
- W3121614855 hasConcept C58471807 @default.
- W3121614855 hasConcept C71924100 @default.
- W3121614855 hasConcept C90924648 @default.
- W3121614855 hasConceptScore W3121614855C121608353 @default.
- W3121614855 hasConceptScore W3121614855C126322002 @default.
- W3121614855 hasConceptScore W3121614855C151956035 @default.
- W3121614855 hasConceptScore W3121614855C2776409635 @default.
- W3121614855 hasConceptScore W3121614855C58471807 @default.
- W3121614855 hasConceptScore W3121614855C71924100 @default.
- W3121614855 hasConceptScore W3121614855C90924648 @default.
- W3121614855 hasIssue "8" @default.
- W3121614855 hasLocation W31216148551 @default.
- W3121614855 hasOpenAccess W3121614855 @default.
- W3121614855 hasPrimaryLocation W31216148551 @default.
- W3121614855 hasRelatedWork W1986785774 @default.
- W3121614855 hasRelatedWork W2007516284 @default.
- W3121614855 hasRelatedWork W2014119546 @default.
- W3121614855 hasRelatedWork W2026086976 @default.
- W3121614855 hasRelatedWork W2117221775 @default.
- W3121614855 hasRelatedWork W2128222437 @default.
- W3121614855 hasRelatedWork W2179876353 @default.
- W3121614855 hasRelatedWork W2565337441 @default.
- W3121614855 hasRelatedWork W2913239611 @default.
- W3121614855 hasRelatedWork W2978608767 @default.
- W3121614855 hasVolume "53" @default.
- W3121614855 isParatext "false" @default.
- W3121614855 isRetracted "false" @default.
- W3121614855 magId "3121614855" @default.
- W3121614855 workType "article" @default.