2019
Enhancing clinical concept extraction with contextual embeddings
Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings. Journal Of The American Medical Informatics Association 2019, 26: 1297-1304. PMID: 31265066, PMCID: PMC6798561, DOI: 10.1093/jamia/ocz096.Peer-Reviewed Original ResearchConceptsClinical concept extractionContextual embeddingsNatural language processing tasksTraditional word embeddingsTraditional word representationsClinical NLP tasksLanguage processing tasksSemantic informationWord embedding methodsLarge language modelsArt performanceConcept extraction taskSemEval 2014Word representationsNLP tasksLanguage modelWord embeddingsProcessing tasksNeural network-based representationI2b2 2010Concept extractionTaskLarge clinical corpusClinical corpusNetwork-based representation
2018
Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition.
Wu Y, Yang X, Bian J, Guo Y, Xu H, Hogan W. Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition. AMIA Annual Symposium Proceedings 2018, 2018: 1110-1117. PMID: 30815153, PMCID: PMC6371322.Peer-Reviewed Original ResearchConceptsRecurrent neural networkWord embeddingsOne-hot vectorsWord representationsLow-frequency wordsOnly word embeddingsClinical Named Entity RecognitionClinical NER tasksWord embedding methodsConditional Random FieldsStatistical language modelNamed Entity RecognitionUnlabeled corpusLanguage modelLanguage systemNER taskDecent representationFactual medical knowledgeImportant wordsDeep learning modelsEntity recognitionClinical corpusNamed Entity Recognition SystemArt performanceFeature representation
2015
A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text.
Wu Y, Xu J, Jiang M, Zhang Y, Xu H. A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text. AMIA Annual Symposium Proceedings 2015, 2015: 1326-33. PMID: 26958273, PMCID: PMC4765694.Peer-Reviewed Original ResearchConceptsNamed Entity RecognitionClinical NER systemNeural word embeddingsClinical Named Entity RecognitionWord embeddingsNER systemWord representationsI2b2 dataEntity recognitionEmbedding featuresClinical textNatural language processing researchConditional Random FieldsLanguage processing researchWord embedding featuresLarge unlabeled corpusBrown clustersNeural wordImportant patient informationFeature representationF1 scoreIntelligent monitoringCritical taskUnlabeled corpusSemantic relations
2014
Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
Tang B, Cao H, Wang X, Chen Q, Xu H. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks. BioMed Research International 2014, 2014: 240403. PMID: 24729964, PMCID: PMC3963372, DOI: 10.1155/2014/240403.Peer-Reviewed Original ResearchConceptsBiomedical Named Entity RecognitionWord representationsNamed Entity Recognition (NER) taskMachine learning-based approachWord representation featuresNatural language processingLearning-based approachEntity recognition taskNamed Entity RecognitionCluster-based representationJNLPBA corpusEntity recognitionBiomedical domainF-measureLanguage processingRepresentation featuresWord embeddingsRecognition taskWR algorithmDistributional representationsTaskBetter performanceAlgorithmRepresentationDifferent types