Entity recognition from clinical texts via recurrent neural network
Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, Xu H. Entity recognition from clinical texts via recurrent neural network. BMC Medical Informatics And Decision Making 2017, 17: 67. PMID: 28699566, PMCID: PMC5506598, DOI: 10.1186/s12911-017-0468-7.Peer-Reviewed Original ResearchConceptsRecurrent neural networkNatural language processingEntity recognitionClinical textTraditional machineNeural networkClinical natural language processingMedical concept extractionHand-crafted featuresClinical entity recognitionDeep learning methodsClinical event detectionConditional Random FieldsSupport vector machineI2b2 NLP challengePerformance of LSTMTypes of entitiesClinical domainsContext informationFeature engineeringConcept extractionDe-identificationEvent detectionKnowledge basesLSTM layersA hybrid approach to automatic de-identification of psychiatric notes
Lee H, Wu Y, Zhang Y, Xu J, Xu H, Roberts K. A hybrid approach to automatic de-identification of psychiatric notes. Journal Of Biomedical Informatics 2017, 75: s19-s27. PMID: 28602904, PMCID: PMC5705430, DOI: 10.1016/j.jbi.2017.06.006.Peer-Reviewed Original ResearchConceptsPsychiatric notesCEGS N-GRIDNatural language processing systemsRule-based componentTask Track 1Language processing systemRule-based approachDe-identificationDomain adaptationRich featuresProcessing systemHybrid approachN gridTrack 1Clinical dataTest setSystem performanceMachineHealth informationHybrid systemSystemClinical applicationTaskInformationData