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 techniquesCombining human and machine intelligence for clinical trial eligibility querying
Fang Y, Idnay B, Sun Y, Liu H, Chen Z, Marder K, Xu H, Schnall R, Weng C. Combining human and machine intelligence for clinical trial eligibility querying. Journal Of The American Medical Informatics Association 2022, 29: 1161-1171. PMID: 35426943, PMCID: PMC9196697, DOI: 10.1093/jamia/ocac051.Peer-Reviewed Original ResearchConceptsNegation scope detectionCohort queriesScope detectionHealth Information Technology Usability Evaluation ScaleHuman-computer collaborationValue normalizationNatural language processingMachine intelligenceDomain expertsEligibility criteria textUsability evaluationLearnability scoreF1 scoreUser interventionLanguage processingHuman intelligenceUsability scoreQueriesError correctionEngagement featuresIntelligenceDisease trialsFrequent modificationsEnhanced modulesCOVID-19 clinical trials
2021
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. Study of Pre-trained Language Models for Named Entity Recognition in Clinical Trial Eligibility Criteria from Multiple Corpora. 2021, 00: 511-512. DOI: 10.1109/ichi52183.2021.00095.Peer-Reviewed Original ResearchA Comparison between Human and NLP-based Annotation of Clinical Trial Eligibility Criteria Text Using The OMOP Common Data Model.
Li X, Liu H, Kury F, Yuan C, Butler A, Sun Y, Ostropolets A, Xu H, Weng C. A Comparison between Human and NLP-based Annotation of Clinical Trial Eligibility Criteria Text Using The OMOP Common Data Model. AMIA Joint Summits On Translational Science Proceedings 2021, 2021: 394-403. PMID: 34457154, PMCID: PMC8378608.Peer-Reviewed Original Research