2020
Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus
Li Y, Luo Y, Wampfler J, Rubinstein S, Tiryaki F, Ashok K, Warner J, Xu H, Yang P. Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus. JCO Clinical Cancer Informatics 2020, 4: cci.19.00147. PMID: 32364754, PMCID: PMC7265793, DOI: 10.1200/cci.19.00147.Peer-Reviewed Original ResearchConceptsNatural language processing toolsElectronic health recordsLanguage processing toolsGold standard dataUnstructured electronic health recordsProcessing toolsAmount of dataClinical notesStandard dataMayo Clinic electronic health recordsClinic's electronic health recordEnvironment toolsAccurate annotationHealth recordsInformatics toolsEffective analysisData setsTextual sourcesCorpusToolInformationData extractionSetExtractingAnnotation
2017
Lightweight predicate extraction for patient-level cancer information and ontology development
Amith M, Song H, Zhang Y, Xu H, Tao C. Lightweight predicate extraction for patient-level cancer information and ontology development. BMC Medical Informatics And Decision Making 2017, 17: 73. PMID: 28699547, PMCID: PMC5506564, DOI: 10.1186/s12911-017-0465-x.Peer-Reviewed Original ResearchConceptsOntological knowledgebaseKnowledge triplesInformation extraction toolsDevelopment of ontologiesNatural language domainRDF representationSoftware libraryOntology developmentCustom applicationsOntologyDevelopment processExtraction toolAccurate extractionPublic health domainKnowledgebaseTextual sourcesTriplesKnowledgebasesHealth domainsToolExtractionTaskMethodsThis paperMedlinePlusDomain