FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912274100> ?p ?o ?g. }

• W2912274100 endingPage "104840" @default.
• W2912274100 startingPage "104840" @default.
• W2912274100 abstract "The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (CRFs) and further transcribing them onto registry databases. To ensure the quality of data, verifying data in the registry is necessary. However, traditional manual data verification methods are time-consuming, labor-intensive and of limited-effect. As paper-based CRFs and electronic medical records (EMRs) are two sources for verification, we propose an automated data verification approach based on the techniques of optical character recognition (OCR) and information retrieval to identify data errors in a registry more efficiently. Three steps are involved to develop the automated verification approach. First, we analyze the scanned images of paper-based CRFs with machine learning enhanced OCR to recognize the checkbox marks and hand-writing. Then, we retrieve the related patient information from the EMRs using natural language processing (NLP) techniques. Finally, we compare the retrieved information in the previous two steps with the data in the registry, and synthesize the results accordingly. The proposed automated method has been applied in a Chinese registry study and the difference between automated and manual approach has been evaluated. The automated approach has been implemented in The Chinese Coronary Artery Disease Registry. For CRF data recognition, the accuracy of recognition for checkboxes marks and hand-writing are 0.93 and 0.74, respectively. For EMR data extraction, the accuracy of information retrieval from textual electronic medical records is 0.97. The accuracy, recall and time consumption of the automated approach are 0.93, 0.96 and 0.5 h, better than the corresponding values of the manual approach, which are 0.92, 0.71 and 7.5 h. Compared to the manual data verification approach, the automated approach enhances the recall of identify data errors and has a higher accuracy. The time consumed is far less. The results show that the automated approach is more effective and efficient for identifying incomplete data and incorrect data in a registry. The proposed approach has potential to improve the quality of registry data." @default.
• W2912274100 created "2019-02-21" @default.
• W2912274100 creator A5041421158 @default.
• W2912274100 creator A5050004899 @default.
• W2912274100 creator A5056801542 @default.
• W2912274100 creator A5057198814 @default.
• W2912274100 creator A5058520218 @default.
• W2912274100 creator A5091670458 @default.
• W2912274100 date "2019-11-01" @default.
• W2912274100 modified "2023-09-26" @default.
• W2912274100 title "An automated data verification approach for improving data quality in a clinical registry" @default.
• W2912274100 cites W1504212872 @default.
• W2912274100 cites W1592724156 @default.
• W2912274100 cites W1597654956 @default.
• W2912274100 cites W2025642731 @default.
• W2912274100 cites W2061020091 @default.
• W2912274100 cites W2094793666 @default.
• W2912274100 cites W2113497436 @default.
• W2912274100 cites W2117692954 @default.
• W2912274100 cites W2158939484 @default.
• W2912274100 cites W2159663991 @default.
• W2912274100 cites W2167025863 @default.
• W2912274100 cites W2190421341 @default.
• W2912274100 cites W2792306126 @default.
• W2912274100 doi "https://doi.org/10.1016/j.cmpb.2019.01.012" @default.
• W2912274100 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30777618" @default.
• W2912274100 hasPublicationYear "2019" @default.
• W2912274100 type Work @default.
• W2912274100 sameAs 2912274100 @default.
• W2912274100 citedByCount "9" @default.
• W2912274100 countsByYear W29122741002019 @default.
• W2912274100 countsByYear W29122741002020 @default.
• W2912274100 countsByYear W29122741002021 @default.
• W2912274100 countsByYear W29122741002022 @default.
• W2912274100 countsByYear W29122741002023 @default.
• W2912274100 crossrefType "journal-article" @default.
• W2912274100 hasAuthorship W2912274100A5041421158 @default.
• W2912274100 hasAuthorship W2912274100A5050004899 @default.
• W2912274100 hasAuthorship W2912274100A5056801542 @default.
• W2912274100 hasAuthorship W2912274100A5057198814 @default.
• W2912274100 hasAuthorship W2912274100A5058520218 @default.
• W2912274100 hasAuthorship W2912274100A5091670458 @default.
• W2912274100 hasConcept C119857082 @default.
• W2912274100 hasConcept C124101348 @default.
• W2912274100 hasConcept C136264566 @default.
• W2912274100 hasConcept C152565575 @default.
• W2912274100 hasConcept C154945302 @default.
• W2912274100 hasConcept C162324750 @default.
• W2912274100 hasConcept C204321447 @default.
• W2912274100 hasConcept C23123220 @default.
• W2912274100 hasConcept C24756922 @default.
• W2912274100 hasConcept C2775953691 @default.
• W2912274100 hasConcept C2780378061 @default.
• W2912274100 hasConcept C41008148 @default.
• W2912274100 hasConcept C49895821 @default.
• W2912274100 hasConceptScore W2912274100C119857082 @default.
• W2912274100 hasConceptScore W2912274100C124101348 @default.
• W2912274100 hasConceptScore W2912274100C136264566 @default.
• W2912274100 hasConceptScore W2912274100C152565575 @default.
• W2912274100 hasConceptScore W2912274100C154945302 @default.
• W2912274100 hasConceptScore W2912274100C162324750 @default.
• W2912274100 hasConceptScore W2912274100C204321447 @default.
• W2912274100 hasConceptScore W2912274100C23123220 @default.
• W2912274100 hasConceptScore W2912274100C24756922 @default.
• W2912274100 hasConceptScore W2912274100C2775953691 @default.
• W2912274100 hasConceptScore W2912274100C2780378061 @default.
• W2912274100 hasConceptScore W2912274100C41008148 @default.
• W2912274100 hasConceptScore W2912274100C49895821 @default.
• W2912274100 hasLocation W29122741001 @default.
• W2912274100 hasLocation W29122741002 @default.
• W2912274100 hasOpenAccess W2912274100 @default.
• W2912274100 hasPrimaryLocation W29122741001 @default.
• W2912274100 hasRelatedWork W2160511800 @default.
• W2912274100 hasRelatedWork W2357241418 @default.
• W2912274100 hasRelatedWork W2961085424 @default.
• W2912274100 hasRelatedWork W3046775127 @default.
• W2912274100 hasRelatedWork W3170094116 @default.
• W2912274100 hasRelatedWork W4285260836 @default.
• W2912274100 hasRelatedWork W4286629047 @default.
• W2912274100 hasRelatedWork W4306321456 @default.
• W2912274100 hasRelatedWork W4306674287 @default.
• W2912274100 hasRelatedWork W4224009465 @default.
• W2912274100 hasVolume "181" @default.
• W2912274100 isParatext "false" @default.
• W2912274100 isRetracted "false" @default.
• W2912274100 magId "2912274100" @default.
• W2912274100 workType "article" @default.