2023
Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study
Bikdeli B, Lo Y, Khairani C, Bejjani A, Jimenez D, Barco S, Mahajan S, Caraballo C, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Wang Y, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Aneja S, Khera R, Goldhaber S, Zhou L, Monreal M, Krumholz H, Piazza G. Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study. Thrombosis And Haemostasis 2023, 123: 649-662. PMID: 36809777, PMCID: PMC11200175, DOI: 10.1055/a-2039-3222.Peer-Reviewed Original ResearchConceptsElectronic health recordsNLP algorithmNatural language processing toolsLanguage processing toolsPrincipal discharge diagnosisICD-10 codesDischarge diagnosisNLP toolsChart reviewHealth systemProcessing toolsYale New Haven Health SystemPatient identificationElectronic databasesHealth recordsData validationHigh-risk PEPulmonary Embolism ResearchSecondary discharge diagnosisIdentification of patientsManual chart reviewNegative predictive valueCodeRadiology reportsAlgorithm
2022
Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports
Torres-Lopez VM, Rovenolt GE, Olcese AJ, Garcia GE, Chacko SM, Robinson A, Gaiser E, Acosta J, Herman AL, Kuohn LR, Leary M, Soto AL, Zhang Q, Fatima S, Falcone GJ, Payabvash MS, Sharma R, Struck AF, Sheth KN, Westover MB, Kim JA. Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports. JAMA Network Open 2022, 5: e2227109. PMID: 35972739, PMCID: PMC9382443, DOI: 10.1001/jamanetworkopen.2022.27109.Peer-Reviewed Original ResearchConceptsNatural language processingF-scoreTest data setsLanguage processingIndependent test data setsData setsBidirectional Encoder RepresentationsAcute brain injuryLarge data setsHead CTBrain injuryNLP toolsF1 scoreNER modelTransformer architectureClinical textEncoder RepresentationsNLP algorithmNLP modelsCT reportsCustom dictionaryTraining setCross-validation performancePerformance metricsAvailable new toolsDiscovering novel drug-supplement interactions using SuppKG generated from the biomedical literature
Schutte D, Vasilakes J, Bompelli A, Zhou Y, Fiszman M, Xu H, Kilicoglu H, Bishop J, Adam T, Zhang R. Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature. Journal Of Biomedical Informatics 2022, 131: 104120. PMID: 35709900, PMCID: PMC9335448, DOI: 10.1016/j.jbi.2022.104120.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemComprehensive knowledge graphDomain terminologyKnowledge graphSemantic relationsNatural language processing technologyLanguage processing technologyNLP toolsDownstream tasksF1 scoreSemantic relationshipsDiscovery patternsPubMed abstractsLimited coverageBiomedical literatureProcessing technologyLanguage systemSemRepDietary supplement informationManual reviewNovel methodologyGraphNodesDomainTaskClinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types.
Eisman A, Brown K, Chen E, Sarkar I. Clinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types. AMIA Annual Symposium Proceedings 2022, 2021: 418-427. PMID: 35308919, PMCID: PMC8861726.Peer-Reviewed Original ResearchConceptsNatural language processingUnified Medical Language SystemNatural language processing tasksMedical Language SystemSources of biomedical dataClinical note sectionsUnified Medical Language System semantic typesHidden Markov ModelNLP toolsLanguage processingBiomedical dataSection detectionClinical notesSemantic typesHMMLanguage systemMedical Information Mart for Intensive Care III
2021
COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model
Wang J, Abu-El-Rub N, Gray J, Pham H, Zhou Y, Manion F, Liu M, Song X, Xu H, Rouhizadeh M, Zhang Y. COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model. Journal Of The American Medical Informatics Association 2021, 28: 1275-1283. PMID: 33674830, PMCID: PMC7989301, DOI: 10.1093/jamia/ocab015.Peer-Reviewed Original ResearchConceptsNatural language processing toolsCommon data modelLanguage processing toolsElectronic health recordsClinical natural language processing toolsData modelDeep learning-based modelProcessing toolsOMOP Common Data ModelPattern-based rulesObservational Medical Outcomes Partnership Common Data ModelLearning-based modelsSpecific information needsUse casesNLP toolsClinical textFree textExtensive evaluationDownloadable packageInformation needsHybrid approachResearch communityHealth recordsData sourcesHigh performance
2020
Generating Training Data for Concept-Mining for an ‘Interface Terminology’ Annotating Cardiology EHRs
Keloth V, Zhou S, Einstein A, Elhanan G, Chen Y, Geller J, Perl Y. Generating Training Data for Concept-Mining for an ‘Interface Terminology’ Annotating Cardiology EHRs. 2020, 00: 1728-1735. DOI: 10.1109/bibm49941.2020.9313435.Peer-Reviewed Original Research
2017
Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.
Kuo T, Rao P, Maehara C, Doan S, Chaparro J, Day M, Farcas C, Ohno-Machado L, Hsu C. Ensembles of NLP Tools for Data Element Extraction from Clinical Notes. AMIA Annual Symposium Proceedings 2017, 2016: 1880-1889. PMID: 28269947, PMCID: PMC5333200.Peer-Reviewed Original ResearchConceptsNatural language processingNLP toolsElectronic health recordsData elementsConcept extractionLanguage processingEnsemble methodDiverse conceptsEvaluation resultsHealth recordsElement extractionClinical notesPlausible solutionToolPipelineExtractionPerformanceEnsembleExtraction performanceConceptNarrative textProcessingText
2014
Natural Language Processing in Biomedicine: A Unified System Architecture Overview
Doan S, Conway M, Phuong T, Ohno-Machado L. Natural Language Processing in Biomedicine: A Unified System Architecture Overview. Methods In Molecular Biology 2014, 1168: 275-294. PMID: 24870142, DOI: 10.1007/978-1-4939-0847-9_16.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsNatural language processingNLP systemsLanguage processingClinical natural language processingBiomedical natural language processingClinical decision supportImportant data sourceArchitectural viewsStructured data fieldsArchitecture overviewData sharingNLP toolsGeneral architectureDecision supportKnowledge resourcesBackground knowledgeData fieldsCollaborative workSystem evaluationData sourcesCurrent research effortsElectronic medical recordsResearch effortsProcessingQuality assurance
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