2024
Relation Extraction
Devarakonda M, Raja K, Xu H. Relation Extraction. Cognitive Informatics In Biomedicine And Healthcare 2024, 101-135. DOI: 10.1007/978-3-031-55865-8_5.Peer-Reviewed Original Research
2020
Relation Extraction from Clinical Narratives Using Pre-trained Language Models.
Wei Q, Ji Z, Si Y, Du J, Wang J, Tiryaki F, Wu S, Tao C, Roberts K, Xu H. Relation Extraction from Clinical Narratives Using Pre-trained Language Models. AMIA Annual Symposium Proceedings 2020, 2019: 1236-1245. PMID: 32308921, PMCID: PMC7153059.Peer-Reviewed Original ResearchConceptsPre-trained language modelsNatural language processingLanguage modelRE tasksNLP tasksClinical narrativesRecent deep learning methodsDeep learning methodsClinical NLP tasksRelation extraction taskTraditional word embeddingsTraditional machineExtraction taskArt performanceRelation extractionBERT modelLanguage processingLearning methodsWord embeddingsShared TaskPrevious stateBiomedical literatureDifferent implementationsTaskOpen domain
2019
Deep learning in clinical natural language processing: a methodical review
Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, Soni S, Wang Q, Wei Q, Xiang Y, Zhao B, Xu H. Deep learning in clinical natural language processing: a methodical review. Journal Of The American Medical Informatics Association 2019, 27: 457-470. PMID: 31794016, PMCID: PMC7025365, DOI: 10.1093/jamia/ocz200.Peer-Reviewed Original ResearchConceptsNatural language processingClinical natural language processingDeep learningLanguage processingComputing Machinery Digital LibraryInformation extraction tasksMedical informatics communityComputational Linguistics anthologyRecurrent neural networkDigital librariesText classificationElectronic health recordsExtraction taskEntity recognitionWord2vec embeddingsNeural networkRelation extractionNLP communityNLP researchInformatics communitySpecific tasksHealth recordsNLP problemLearningClinical domainsDeveloping Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP
Soysal E, Warner J, Wang J, Jiang M, Harvey K, Jain S, Dong X, Song H, Siddhanamatha H, Wang L, Dai Q, Chen Q, Du X, Tao C, Yang P, Denny J, Liu H, Xu H. Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP. 2019, 264: 1041-1045. PMID: 31438083, PMCID: PMC7359882, DOI: 10.3233/shti190383.Peer-Reviewed Original ResearchConceptsElectronic health recordsNLP solutionNatural language processing technologyInformation extraction moduleLanguage processing technologyInformation extraction tasksUser-friendly interfaceBest F-measureInformation extractionExtraction moduleExtraction taskCustomizable modulesNLP systemsF-measureAcademic useHealth recordsComparable performanceProcessing technologyVanderbilt University Medical CenterModuleDiverse typesInformationNLPSubstantial effortSystem
2015
A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature
Tang B, Feng Y, Wang X, Wu Y, Zhang Y, Jiang M, Wang J, Xu H. A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature. Journal Of Cheminformatics 2015, 7: s8. PMID: 25810779, PMCID: PMC4331698, DOI: 10.1186/1758-2946-7-s1-s8.Peer-Reviewed Original ResearchMachine learning-based systemsConditional Random FieldsLearning-based systemEntity recognition systemSupport vector machineEntity recognitionRecognition systemF-measureChallenge organizersDrug Named Entity RecognitionVector machineStructured support vector machineMicro F-measureInformation extraction tasksWord representation featuresNamed Entity RecognitionTest setRandom fieldsPrimary evaluation measureBrown clusteringDocument indexingIndividual subtasksExtraction taskRandom IndexingBiomedical domain
2012
Clinical entity recognition using structural support vector machines with rich features
Tang B, Cao H, Wu Y, Jiang M, Xu H. Clinical entity recognition using structural support vector machines with rich features. 2012, 13-20. DOI: 10.1145/2390068.2390073.Peer-Reviewed Original ResearchStructural support vector machineClinical entity recognitionSupport vector machineConditional Random FieldsNatural language processingEntity recognitionVector machineRich featuresNLP challengeSequential labeling algorithmLarge margin theoryUnsupervised word representationsClinical text processingConcept extraction taskLess training timeHighest F-measureTest setI2b2 NLP challengeExtraction taskTypical machineNER taskClinical textTraining timeF-measureLanguage processing