<> <http://www.w3.org/2000/01/rdf-schema#comment> "The repository administrator has not yet configured an RDF license."^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/eprint/10094398> <http://purl.org/dc/terms/title> "Validation of an algorithm to evaluate the appropriateness of outpatient antibiotic prescribing using big data of Chinese diagnosis text"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10094398> <http://purl.org/ontology/bibo/abstract> "OBJECTIVE: We aimed to evaluate the validity of an algorithm to classify diagnoses according to the appropriateness of outpatient antibiotic use in the context of Chinese free text. SETTING AND PARTICIPANTS: A random sample of 10 000 outpatient visits was selected between January and April 2018 from a national database for monitoring rational use of drugs, which included data from 194 secondary and tertiary hospitals in China. RESEARCH DESIGN: Diagnoses for outpatient visits were classified as tier 1 if associated with at least one condition that 'always' justified antibiotic use; as tier 2 if associated with at least one condition that only 'sometimes' justified antibiotic use but no conditions that 'always' justified antibiotic use; or as tier 3 if associated with only conditions that never justified antibiotic use, using a tier-fashion method and regular expression (RE)-based algorithm. MEASURES: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the classification algorithm, using classification made by chart review as the standard reference, were calculated. RESULTS: The sensitivities of the algorithm for classifying tier 1, tier 2 and tier 3 diagnoses were 98.2% (95% CI 96.4% to 99.3%), 98.4% (95% CI 97.6% to 99.1%) and 100.0% (95% CI 100.0% to 100.0%), respectively. The specificities were 100.0% (95% CI 100.0% to 100.0%), 100.0% (95% CI 99.9% to 100.0%) and 98.6% (95% CI 97.9% to 99.1%), respectively. The PPVs for classifying tier 1, tier 2 and tier 3 diagnoses were 100.0% (95% CI 99.1% to 100.0%), 99.7% (95% CI 99.2% to 99.9%) and 99.7% (95% CI 99.6% to 99.8%), respectively. The NPVs were 99.9% (95% CI 99.8% to 100.0%), 99.8% (95% CI 99.7% to 99.9%) and 100.0% (95% CI 99.8% to 100.0%), respectively. CONCLUSIONS: The RE-based classification algorithm in the context of Chinese free text had sufficiently high validity for further evaluating the appropriateness of outpatient antibiotic prescribing."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10094398> <http://purl.org/dc/terms/date> "2020-03-19" .
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<https://discovery.ucl.ac.uk/id/person/ext-7dc20431d597baab1e88dce2190c1255> <http://xmlns.com/foaf/0.1/givenName> "S"^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/person/ext-7dc20431d597baab1e88dce2190c1255> <http://xmlns.com/foaf/0.1/name> "S Zhan"^^<http://www.w3.org/2001/XMLSchema#string> .
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