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 wayTaskAttributesClassificationIntegrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text
Li Z, Yang Z, Shen C, Xu J, Zhang Y, Xu H. Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text. BMC Medical Informatics And Decision Making 2019, 19: 22. PMID: 30700301, PMCID: PMC6354333, DOI: 10.1186/s12911-019-0736-9.Peer-Reviewed Original ResearchConceptsShortest dependency pathConvolutional neural networkNeural network architectureNatural language processingSentence sequenceRelation extractionClinical relation extractionTarget entityNetwork architectureClinical textNeural networkRepresentation moduleDependency pathsDeep learning-based approachNew neural network architectureBidirectional long short-term memory networkLong short-term memory networkDeep learning frameworkDeep neural networksShort-term memory networkLearning-based approachNovel neural approachRelation extraction datasetBi-LSTM networkSyntactic features