2023
Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach
Hu Y, Keloth V, Raja K, Chen Y, Xu H. Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach. Bioinformatics 2023, 39: btad542. PMID: 37669123, PMCID: PMC10500081, DOI: 10.1093/bioinformatics/btad542.Peer-Reviewed Original ResearchNatural language processingMicro-F1 scoreCOVID-19 datasetNLP pipelineF1 scoreEntity recognition modelAD datasetPICO elementsSentence classificationNER modelRecognition modelLanguage processingLearning approachLearning modelEnd evaluationSupplementary dataDatasetPipelineExtractionInformationRCT abstractsAnnotationSentencesBioinformaticsComplexity
2019
A study of deep learning approaches for medication and adverse drug event extraction from clinical text
Wei Q, Ji Z, Li Z, Du J, Wang J, Xu J, Xiang Y, Tiryaki F, Wu S, Zhang Y, Tao C, Xu H. A study of deep learning approaches for medication and adverse drug event extraction from clinical text. Journal Of The American Medical Informatics Association 2019, 27: 13-21. PMID: 31135882, PMCID: PMC6913210, DOI: 10.1093/jamia/ocz063.Peer-Reviewed Original ResearchConceptsDeep learning-based approachDeep learning approachLearning-based approachTraditional machineLearning approachNational NLP Clinical ChallengesAdverse drug event extractionOutperform traditional machineDifferent ensemble approachesConditional Random FieldsSequence labeling approachMIMIC-III databaseEvent extractionMedical domainEntity recognitionClassification componentF1 scoreClinical textRelation extractionClinical documentsVector machineEnd evaluationEnsemble approachClinical corpusMachine