2022
ClinicalLayoutLM: A Pre-trained Multi-modal Model for Understanding Scanned Document in Electronic Health Records
Wei Q, Zuo X, Anjum O, Hu Y, Denlinger R, Bernstam E, Citardi M, Xu H. ClinicalLayoutLM: A Pre-trained Multi-modal Model for Understanding Scanned Document in Electronic Health Records. 2022, 00: 2821-2827. DOI: 10.1109/bigdata55660.2022.10020569.Peer-Reviewed Original ResearchOptical character recognitionMulti-modal modelElectronic health recordsClinical documentsNatural language processing tasksInformation extraction technologyPre-trained modelsHealth recordsLanguage processing tasksInformation extractionImage informationF1 scoreCharacter recognitionLayout analysisProcessing tasksMulti-modal approachClinical corpusBaseline modelDocumentsOpen domainTaskExtraction technologyClinical operationsDifferent categoriesTextNatural Language Processing
Xu H, Roberts K. Natural Language Processing. Cognitive Informatics In Biomedicine And Healthcare 2022, 213-234. DOI: 10.1007/978-3-031-09108-7_7.Peer-Reviewed Original ResearchNatural language processingLanguage processingElectronic health recordsBiomedical domainBiomedical natural language processingCommon NLP tasksNarrative textNLP tasksBiomedical articlesClinical documentsNLP fieldTextHealth recordsLarge amountBasic conceptsBibliographic databasesProcessingTaskArticleDocumentsDomainChapterDatabaseInformationAttention
2019
A 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
2015
A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time
Wu Y, Denny J, Rosenbloom S, Miller R, Giuse D, Song M, Xu H. A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time. Applied Clinical Informatics 2015, 06: 364-374. PMID: 26171081, PMCID: PMC4493336, DOI: 10.4338/aci-2014-10-ra-0088.Peer-Reviewed Original ResearchConceptsElectronic health record systemsUser studyClinical documentation systemNatural language processing systemsClinical NLP systemsPreliminary user studyAbbreviation recognitionExtra time costLanguage processing systemWSD methodHealth record systemsDocumentation systemPrototype applicationWord sense disambiguation methodNLP systemsCorrect sensesNote generationPrototype systemClinical sentencesCost of timeClinical documentsDocument entryDisambiguation moduleSense disambiguation methodHealthcare recordsNamed Entity Recognition in Chinese Clinical Text Using Deep Neural Network.
Wu Y, Jiang M, Lei J, Xu H. Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. 2015, 216: 624-8. PMID: 26262126, PMCID: PMC4624324.Peer-Reviewed Original ResearchConceptsDeep neural networksLarge unlabeled corpusNamed Entity RecognitionWord embeddingsUnlabeled corpusUnsupervised learningEntity recognitionNeural networkNatural language processing technologyNovel deep learning methodLanguage processing technologyDeep learning methodsUnsupervised feature learningFeature engineering approachImportant healthcare informationChinese clinical textTypes of entitiesFeature learningNER taskClinical textLearning methodsClinical documentsCRF modelHealthcare informationFree text
2013
Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing.
Moon S, Berster B, Xu H, Cohen T. Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing. AMIA Annual Symposium Proceedings 2013, 2013: 1007-16. PMID: 24551390, PMCID: PMC3900125.Peer-Reviewed Original ResearchConceptsWord sense disambiguationAverage accuracySense disambiguationWord sense disambiguation algorithmSupport vector machineHyperdimensional ComputingNaïve BayesCommon machineClinical documentsVector machineDisambiguation algorithmClinical abbreviationsMedical informationAccurate extractionAlgorithmDisambiguationMachineSuch approachesClinical notesPresent new approachVector transformationNew approachAmbiguous termsComputingAccuracyA prototype application for real-time recognition and disambiguation of clinical abbreviations
Wu Y, Denny J, Rosenbloom S, Miller R, Giuse D, Song M, Xu H. A prototype application for real-time recognition and disambiguation of clinical abbreviations. 2013, 7-8. DOI: 10.1145/2512089.2512096.Peer-Reviewed Original ResearchElectronic health record systemsPrototype applicationClinical documentation systemNatural language processing systemsClinical abbreviationsClinical NLP systemsReal-time recognitionLanguage processing systemAverage response timeHealth record systemsDocumentation systemResponse timeWord sense disambiguation methodNLP systemsNote generationPrototype systemClinical documentsSense disambiguation methodHealthcare recordsProcessing systemAbbreviation disambiguationCard systemDisambiguation methodAbbreviation recognitionSystem designAnalyzing differences between chinese and english clinical text: a cross-institution comparison of discharge summaries in two languages.
Wu Y, Lei J, Wei W, Tang B, Denny J, Rosenbloom S, Miller R, Giuse D, Zheng K, Xu H. Analyzing differences between chinese and english clinical text: a cross-institution comparison of discharge summaries in two languages. 2013, 192: 662-6. PMID: 23920639, PMCID: PMC4957806.Peer-Reviewed Original ResearchConceptsNatural language processing toolsEnglish clinical textClinical textLanguage processing toolsChinese clinical textCultural differencesMajor clinical componentsTextWestern institutionsInpatient discharge summariesCross-country collaborationDocument levelProcessing toolsClinical documentsLanguageUS institutionsUsesUnprecedented amountValuable insightsInstitutionsDocumentsChinaWorldwide adoptionEMR dataCollaboration