2024
Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms
Tan A, Gonçalves R, Yuan W, Brat G, Gentleman R, Kohane I, Masino A, Makoudjou A, Albayrak A, Gutiérrez-Sacristán A, Zambelli A, Malovini A, Carmona A, Hoffmann A, Gramfort A, Geva A, Blanco-Martínez A, Tan A, Terriza-Torres A, Spiridou A, Prunotto A, South A, Vallejos A, Atz A, Burgun A, Alloni A, Cattelan A, Jannot A, Neuraz A, Bellasi A, Maram A, Dagliati A, Sandrin A, Serret-Larmande A, Mensch A, Pfaff A, Batugo A, Krishnamurthy A, Adam A, Dionne A, Devkota B, Moal B, He B, Beaulieu-Jones B, Beaulieu-Jones B, Ostasiewski B, Aronow B, Tan B, Tan B, Torti C, Sáez C, Neto C, Sonday C, Caucheteux C, Mao C, Zucco C, Daniel C, Haverkamp C, Hong C, Bonzel C, Moraleda C, Leprovost D, Key D, Zöller D, Pillion D, Mowery D, Amendola D, Henderson D, Hanauer D, Taylor D, Wassermann D, Hazard D, Kraska D, Mazzotti D, Silvio D, Bell D, Murad D, Salamanca E, Bucholz E, Getzen E, Pfaff E, Schriver E, Toh E, Parimbelli E, Trecarichi E, Ashraf F, Vidorreta F, Bourgeois F, Sperotto F, Angoulvant F, Brat G, Varoquaux G, Omenn G, Agapito G, Albi G, Weber G, Verdy G, Lemaitre G, Roig-Domínguez G, Prokosch H, Zhang H, Estiri H, Krantz I, Kohane I, Honerlaw J, Cruz-Rojo J, Norman J, Balshi J, Cimino J, Aaron J, Santos J, Newburger J, Zahner J, Moore J, Marwaha J, Craig J, Klann J, Morris J, Obeid J, Vie J, Chen J, Son J, Zachariasse J, Booth J, Holmes J, Bernal-Sobrino J, Cruz-Bermúdez J, Leblanc J, Schuettler J, Dubiel J, Champ J, Olson K, Moshal K, Kernan K, Kirchoff K, Wagholikar K, Ngiam K, Cho K, Mandl K, Huling K, Chen K, Lynch K, Sanchez-Pinto L, Garmire L, Han L, Patel L, Waitman L, Lenert L, Anthony L, Esteve L, Chiudinelli L, Chiovato L, Scudeller L, Samayamuthu M, Martins M, Minicucci M, Menezes M, Vella M, Mazzitelli M, Savino M, Milano M, Okoshi M, Cannataro M, Alessiani M, Keller M, Hilka M, Wolkewitz M, Boeker M, Raskin M, Bucalo M, Hutch M, Bernaux M, Beraghi M, Morris M, Vitacca M, Pedrera-Jiménez M, Daniar M, Shah M, Liu M, Maripuri M, Kainth M, Yehya N, Santhanam N, Palmer N, Loh N, Sebire N, Romero-Garcia N, Brown N, Paris N, Griffon N, Gehlenborg N, Orlova N, García-Barrio N, Grisel O, Rojo P, Serrano-Balazote P, Sacchi P, Tippmann P, Martel P, Serre P, Avillach P, Azevedo P, Rubio-Mayo P, Schubert P, Guzzi P, Sliz P, Das P, Long Q, Ramoni R, Goh R, Badenes R, Bruno R, Kavuluru R, Bellazzi R, Issitt R, Follett R, Bradford R, Prudente R, Bey R, Griffier R, Duan R, Mahmood S, Mousavi S, Lozano-Zahonero S, Pizzimenti S, Maidlow S, Wong S, DuVall S, Cossin S, L'Yi S, Murphy S, Fan S, Visweswaran S, Rieg S, Bosari S, Makwana S, Bréant S, Bhatnagar S, Tanni S, Cormont S, Ahooyi T, Priya T, Naughton T, Ganslandt T, Colicchio T, Cai T, Gradinger T, González T, Zuccaro V, Tibollo V, Jouhet V, Quirós-González V, Panickan V, Benoit V, Njoroge W, Bryant W, Yuan W, Xiong X, Wang X, Ye Y, Luo Y, Ho Y, Strasser Z, Abad Z, Xia Z, Kate K, Hernández-Arango A, Schwamm E. Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms. JAMIA Open 2024, 7: ooae118. PMID: 39559493, PMCID: PMC11570992, DOI: 10.1093/jamiaopen/ooae118.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record datasetInternational Classification of Diseases codesInternational Classification of DiseasesAlignment of ontologiesMedical Language SystemHuman Phenotype OntologyData annotationBiomedical entitiesUMLData integrationElectronic health record dataInternational ClassificationHuman Phenotype Ontology termsHealth recordsOntologyCodeAnnotated phenotypesClinical diagnosis codesClassification of diseasesLanguage systemDatasetResearch ontologyMap coverageDiagnosis codes
2023
LeafAI: query generator for clinical cohort discovery rivaling a human programmer
Dobbins N, Han B, Zhou W, Lan K, Kim H, Harrington R, Uzuner Ö, Yetisgen M. LeafAI: query generator for clinical cohort discovery rivaling a human programmer. Journal Of The American Medical Informatics Association 2023, 30: 1954-1964. PMID: 37550244, PMCID: PMC10654856, DOI: 10.1093/jamia/ocad149.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemHuman programmersQuery creationDatabase programmersText processing problemsUnified Medical Language System conceptsState-of-the-artLogical reasoning capabilityKnowledge baseMedical Language SystemRule-based moduleHybrid deep learningQuery generationSchema elementsEntity recognitionReasoning capabilitiesQuery designDeep learningQueryCohort discoveryClinical trial eligibility criteriaLanguage systemConditional reasoningClinical trialsSequence-to-sequence transformation
2022
Discovering 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 methodologyGraphNodesDomainTaskStandardization of presurgical language fMRI in Greek population: Mapping of six critical regions
Gkiatis K, Garganis K, Benjamin CF, Karanasiou I, Kondylidis N, Harushukuri J, Matsopoulos GK. Standardization of presurgical language fMRI in Greek population: Mapping of six critical regions. Brain And Behavior 2022, 12: e2609. PMID: 35587046, PMCID: PMC9226851, DOI: 10.1002/brb3.2609.Peer-Reviewed Original ResearchConceptsLanguage mappingFunctional magnetic resonance imagingPresurgical language mappingMother tongueLanguage systemWernicke-LichtheimLanguage regionsLanguage functional magnetic resonance imagingGreekLanguage lateralizationHealthy right-handed volunteersGreek populationRight-handed volunteersMagnetic resonance imagingClinical setupEloquent cortexFMRI acquisitionContralateral cerebellumPresurgical evaluationAppropriate tasksGreek patientsWada testResonance imagingNoninvasive alternativeFMRI protocolClinical 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
Monolingual and bilingual language networks in healthy subjects using functional MRI and graph theory
Li Q, Pasquini L, Del Ferraro G, Gene M, Peck K, Makse H, Holodny A. Monolingual and bilingual language networks in healthy subjects using functional MRI and graph theory. Scientific Reports 2021, 11: 10568. PMID: 34012006, PMCID: PMC8134560, DOI: 10.1038/s41598-021-90151-4.Peer-Reviewed Original ResearchConceptsLanguage networkLanguage tasksNative English-speaking monolingualsPre-supplementary motor areaEnglish-speaking monolingualsInfluence of bilingualismFMRI language tasksNative Spanish-speakersFMRI taskLanguage systemBroca's areaWernicke's areaFunctional MRISpanish-speakingMotor areaPremotor areasConnectivity metricsBilingualsFunctional networksFMRIL2Neurological historyActive clustersTaskK-core analysisInterpersonal Agreement and Disagreement During Face-to-Face Dialogue: An fNIRS Investigation
Hirsch J, Tiede M, Zhang X, Noah JA, Salama-Manteau A, Biriotti M. Interpersonal Agreement and Disagreement During Face-to-Face Dialogue: An fNIRS Investigation. Frontiers In Human Neuroscience 2021, 14: 606397. PMID: 33584223, PMCID: PMC7874076, DOI: 10.3389/fnhum.2020.606397.Peer-Reviewed Original ResearchSupramarginal gyrusAttention networkBilateral dorsolateral prefrontal cortexLanguage-related processesCanonical language areasVisual attention networkRight supramarginal gyrusDorsolateral prefrontal cortexSuperior temporal gyrusFNIRS investigationFrontoparietal systemNeural correlatesSocial cuesAngular gyrusTemporal gyrusPrefrontal cortexLanguage areasFrontopolar regionNeural systemsSyllable rateNeural activityMedian fundamental frequencyFace conversationNeural couplingLanguage system
2020
A review of auditing techniques for the Unified Medical Language System
Zheng L, He Z, Wei D, Keloth V, Fan JW, Lindemann L, Zhu X, Cimino JJ, Perl Y. A review of auditing techniques for the Unified Medical Language System. Journal Of The American Medical Informatics Association 2020, 27: 1625-1638. PMID: 32766692, PMCID: PMC7566540, DOI: 10.1093/jamia/ocaa108.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemAuditing techniquesLevel of automationLanguage systemOntology alignmentUMLS MetathesaurusError detectionConcept namesAlignment techniqueSemantic type assignmentsOntology enrichmentTypes of knowledgeHierarchical relationshipsEasy accessSummarizationPRISMA approachDevelopment of methodsMetathesaurusQuality assuranceAutomationTechniqueConceptual aspectsSystemWealth of knowledgeConcept
2019
Alternative classification of identical concepts in different terminologies: Different ways to view the world
Keloth V, He Z, Elhanan G, Geller J. Alternative classification of identical concepts in different terminologies: Different ways to view the world. Journal Of Biomedical Informatics 2019, 94: 103193. PMID: 31048072, PMCID: PMC7050413, DOI: 10.1016/j.jbi.2019.103193.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemHuman expertsMedical Language SystemPairs of terminologiesParent conceptClassificationMedical terminologyID numberLanguage systemInvestigate different kindsIdentity conceptDifferent kindsAlternative classificationExpression criteriaChildren's conceptionsExpertsConceptMathematical quantitiesTerminologyGranularity
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 representationExtended Analysis of Topological-Pattern-Based Ontology Enrichment
He Z, Keloth V, Chen Y, Geller J. Extended Analysis of Topological-Pattern-Based Ontology Enrichment. 2024 IEEE International Conference On Bioinformatics And Biomedicine (BIBM) 2018, 00: 1641-1648. PMID: 30854243, PMCID: PMC6402505, DOI: 10.1109/bibm.2018.8621564.Peer-Reviewed Original ResearchUnified Medical Language SystemTarget ontologyNational Cancer Institute ThesaurusMaintenance of biomedical ontologiesPairs of ontologiesMedical Language SystemHuman expertsCross-ontologyReference ontologyBiomedical ontologiesOntologyLanguage systemExpert resourcesHierarchical relationshipsTheoretical analysisApproximate locationTopological patternsThesaurusReference conceptTaskConceptClassificationMethodUnified
2016
Language system organization in a quadrilingual with a brain tumor: Implications for understanding of the language network
Połczyńska MM, Benjamin CF, Japardi K, Frew A, Bookheimer SY. Language system organization in a quadrilingual with a brain tumor: Implications for understanding of the language network. Neuropsychologia 2016, 86: 167-175. PMID: 27143224, DOI: 10.1016/j.neuropsychologia.2016.04.030.Peer-Reviewed Original ResearchConceptsLanguage mappingPre-operative functional magnetic resonance imagingLanguage-specific regionsFunctional magnetic resonance imagingAge of acquisitionIntraoperative language mappingAmount of exposureMultilingual speakersSecond languageFourth languageMultilingual individualsFrontal brain tumorLanguage similarityLanguage systemNeurocognitive modelsLanguageLanguage networkAge 5Different organizationsSpeakersMagnetic resonance imagingResonance imagingImpairmentSystem organizationOrganization
2012
Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification
Garla V, Brandt C. Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification. 2012, 1: 22-22. DOI: 10.1109/hisb.2012.12.Peer-Reviewed Original ResearchClinical document classificationUnified Medical Language SystemDocument classificationNatural language processing systemsClinical text classificationWSD systemsMachine Learning ClassifiersText processing tasksLanguage processing systemSemantic similarity measuresKnowledge-based methodsText classificationLearning classifiersWord sense disambiguation methodOpen sourceProcessing tasksSimilarity measureSense disambiguation methodProcessing systemWSD datasetDisambiguation methodLanguage systemWSD methodClassificationMedicine challenges
2007
A study of abbreviations in clinical notes.
Xu H, Stetson P, Friedman C. A study of abbreviations in clinical notes. AMIA Annual Symposium Proceedings 2007, 2007: 821-5. PMID: 18693951, PMCID: PMC2655910.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemNatural language processing systemsLanguage processing systemNarrative clinical notesDetection methodClinical notesDifferent knowledge sourcesSense inventoryDomain expertsNLP systemsCorrect sensesDecision supportText corporaKnowledge sourcesError detectionProcessing systemBiomedical literatureStudy of abbreviationsLanguage systemPatient informationAmbiguity rateBetter detection methodsDatabaseAnnotationAbbreviationsUsing contextual and lexical features to restructure and validate the classification of biomedical concepts
Fan J, Xu H, Friedman C. Using contextual and lexical features to restructure and validate the classification of biomedical concepts. BMC Bioinformatics 2007, 8: 264. PMID: 17650333, PMCID: PMC2014782, DOI: 10.1186/1471-2105-8-264.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemString-based approachesMean reciprocal rankReciprocal rankNatural language processingError rateContextual featuresLexical featuresIntegration of dataLow error rateReasoning systemAutomatic approachComplementary classifiersLanguage processingClassification approachBiomedical terminologiesClassification errorOntological conceptsBiomedical conceptsOntological termsSyntactic approachLanguage systemClassifierSyntactic featuresOntology
2006
The Practical Impact of Ontologies on Biomedical Informatics
Cimino J, Zhu X. The Practical Impact of Ontologies on Biomedical Informatics. Yearbook Of Medical Informatics 2006, 15: 124-135. PMID: 17051306, DOI: 10.1055/s-0038-1638470.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemMedical Entities DictionaryBiomedical information systemsFull-text searchProtégé toolEntities DictionaryBiomedical ontologiesTerm ontologyRecent research workInformation systemsBiomedical terminologiesBiomedical informaticsOntologySNOMED CTFoundational ModelNCI ThesaurusNDF-RTTerminology modelLanguage systemTerminology projectsDefinitional knowledgeResearch workPractical impactRecent orientation
2005
Language network specializations: An analysis with parallel task designs and functional magnetic resonance imaging
Gitelman D, Nobre A, Sonty S, Parrish T, Mesulam M. Language network specializations: An analysis with parallel task designs and functional magnetic resonance imaging. NeuroImage 2005, 26: 975-985. PMID: 15893473, DOI: 10.1016/j.neuroimage.2005.03.014.Peer-Reviewed Original ResearchConceptsInferior parietal areasTask-specific activationLanguage tasksParietal areasLanguage systemFunctional magnetic resonance imagingCore language regionsMid-temporalInferior frontal cortexAssociated with particular aspectsPhonological tasksOccipito-temporalFrontal cortexLanguage regionsAdvent of functional imagingBrain activityExperimental sessionsLanguageMagnetic resonance imagingSubject groupsFunctional imagingTaskSegregation activityBrainResonance imagingLanguage Development in Preschool-Age Children Adopted From China
Roberts J, Pollock K, Krakow R, Price J, Fulmer K, Wang P. Language Development in Preschool-Age Children Adopted From China. Journal Of Speech Language And Hearing Research 2005, 48: 93-107. PMID: 15938062, DOI: 10.1044/1092-4388(2005/008).Peer-Reviewed Original Research
2001
The neurobiology of dyslexia
Shaywitz B, Shaywitz S, Pugh K, Fulbright R, Mencl W, Constable R, Skudlarski P, Fletcher J, Lyon G, Gore J. The neurobiology of dyslexia. Clinical Neuroscience Research 2001, 1: 291-299. DOI: 10.1016/s1566-2772(01)00015-9.Peer-Reviewed Original ResearchPhonological processingNeural systemsNeurobiology of dyslexiaPosterior brain systemsFrontal lobe circuitsDiagnosis of dyslexiaFunctional brain imagingDevelopment of tasksSubcomponent processesCognitive modelCognitive basisReading taskBrain systemsNeural substratesDyslexiaBrain organizationParticular subcomponentLanguage systemBrain imagingSpecific hypothesesTaskAdditional suggestionsReadingProcessingAdults
1998
PASSIVE LISTENING DURING fMRI REVEALS AN EXTENSIVE RECEPTIVE LANGUAGE SYSTEM IN YOUNG AND SEDATED CHILDREN
Hirsch J, Kim K, Souweidane M, Ruge M, Correa D, Moreno D, McDowall R, Krol G. PASSIVE LISTENING DURING fMRI REVEALS AN EXTENSIVE RECEPTIVE LANGUAGE SYSTEM IN YOUNG AND SEDATED CHILDREN. NeuroImage 1998, 7: s513. DOI: 10.1016/s1053-8119(18)31346-6.Peer-Reviewed Original Research
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