2023
AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models
Datta S, Lee K, Paek H, Manion F, Ofoegbu N, Du J, Li Y, Huang L, Wang J, Lin B, Xu H, Wang X. AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models. Journal Of The American Medical Informatics Association 2023, 31: 375-385. PMID: 37952206, PMCID: PMC10797270, DOI: 10.1093/jamia/ocad218.Peer-Reviewed Original ResearchConceptsLanguage modelInformation extraction systemOverall F1 scoreCriteria informationF1 scoreManual annotationScalable solutionContextual informationComplex scenariosContextual attributesExtraction systemReal-world settingsSystem evaluationModeling capabilitiesClinical trial protocol documentsInformationProtocol documents
2022
A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora
Li J, Wei Q, Ghiasvand O, Chen M, Lobanov V, Weng C, Xu H. A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora. BMC Medical Informatics And Decision Making 2022, 22: 235. PMID: 36068551, PMCID: PMC9450226, DOI: 10.1186/s12911-022-01967-7.Peer-Reviewed Original ResearchConceptsPre-trained language modelsNER taskUnstructured textEntity recognitionLanguage modelNatural language processing techniquesClinical trial eligibility criteriaLanguage processing techniquesData augmentation resultsData augmentation approachDomain-specific corpusBetter performanceTransformer modelCross-validation showMultiple data sourcesEligibility criteria textBiomedical domainEmbedding modelsNER performanceAugmentation approachContextual embeddingsMeaningful informationEvaluation resultsSuch documentsProcessing techniques
2017
Expressing Biomedical Ontologies in Natural Language for Expert Evaluation.
Amith M, Manion F, Harris M, Zhang Y, Xu H, Tao C. Expressing Biomedical Ontologies in Natural Language for Expert Evaluation. 2017, 245: 838-842. PMID: 29295217, PMCID: PMC6644701.Peer-Reviewed Original Research
2015
Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.
Wu Y, Jiang M, Lei J, Xu H. Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. 2015, 216: 624-8. PMID: 26262126, PMCID: PMC4624324.Peer-Reviewed Original ResearchConceptsDeep neural networksLarge unlabeled corpusNamed Entity RecognitionWord embeddingsUnlabeled corpusUnsupervised learningEntity recognitionNeural networkNatural language processing technologyNovel deep learning methodLanguage processing technologyDeep learning methodsUnsupervised feature learningFeature engineering approachImportant healthcare informationChinese clinical textTypes of entitiesFeature learningNER taskClinical textLearning methodsClinical documentsCRF modelHealthcare informationFree text