Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text
Xu J, Li Z, Wei Q, Wu Y, Xiang Y, Lee H, Zhang Y, Wu S, Xu H. Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text. BMC Medical Informatics And Decision Making 2019, 19: 236. PMID: 31801529, PMCID: PMC6894107, DOI: 10.1186/s12911-019-0937-2.Peer-Reviewed Original ResearchConceptsSequence labeling approachMedical conceptsEntity recognitionRelation classificationClinical textDetection taskBidirectional long short-term memory networkLong short-term memory networkShort-term memory networkConditional Random FieldsSequence labeling problemTraditional methodsNLP applicationsBi-LSTMNeural architectureLabeling problemLabeling approachMemory networkNovel solutionRandom fieldsHigh accuracyEfficient wayTaskAttributesClassificationA 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