2024
Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition
Zuo X, Kumar A, Shen S, Li J, Cong G, Jin E, Chen Q, Warner J, Yang P, Xu H. Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition. JCO Clinical Cancer Informatics 2024, 8: e2300166. PMID: 38885475, DOI: 10.1200/cci.23.00166.Peer-Reviewed Original ResearchConceptsNatural language processingDomain-specific language modelsNatural language processing systemsInformation extraction systemRule-based moduleNarrative clinical textsNLP tasksEntity recognitionText normalizationAssertion classificationLanguage modelInformation extractionClinical textElectronic health recordsLearning-basedClinical notesLanguage processingTest setSystem performanceHealth recordsResponse extractionTime-consumingAnticancer therapyInformationAssessment informationNLP Applications—Other Biomedical Texts
Roberts K, Xu H, Demner Fushman D. NLP Applications—Other Biomedical Texts. Cognitive Informatics In Biomedicine And Healthcare 2024, 429-444. DOI: 10.1007/978-3-031-55865-8_15.Peer-Reviewed Original ResearchStandardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach.
Zuo X, Zhou Y, Duke J, Hripcsak G, Shah N, Banda J, Reeves R, Miller T, Waitman L, Natarajan K, Xu H. Standardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach. AMIA Annual Symposium Proceedings 2024, 2023: 834-843. PMID: 38222429, PMCID: PMC10785935.Peer-Reviewed Original Research
2023
Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO).
Dang Y, Li F, Hu X, Keloth V, Zhang M, Fu S, Amith M, Fan J, Du J, Yu E, Liu H, Jiang X, Xu H, Tao C. Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO). Journal Of The American Medical Informatics Association 2023, 30: 1465-1473. PMID: 37301740, PMCID: PMC10436148, DOI: 10.1093/jamia/ocad096.Peer-Reviewed Original ResearchRepresenting and utilizing clinical textual data for real world studies: An OHDSI approach
Keloth V, Banda J, Gurley M, Heider P, Kennedy G, Liu H, Liu F, Miller T, Natarajan K, V Patterson O, Peng Y, Raja K, Reeves R, Rouhizadeh M, Shi J, Wang X, Wang Y, Wei W, Williams A, Zhang R, Belenkaya R, Reich C, Blacketer C, Ryan P, Hripcsak G, Elhadad N, Xu H. Representing and utilizing clinical textual data for real world studies: An OHDSI approach. Journal Of Biomedical Informatics 2023, 142: 104343. PMID: 36935011, PMCID: PMC10428170, DOI: 10.1016/j.jbi.2023.104343.Peer-Reviewed Original ResearchConceptsNatural language processingCommon data modelTextual dataNLP solutionObservational Health Data SciencesOMOP Common Data ModelSpecific use casesObservational Medical Outcomes Partnership Common Data ModelHealth Data SciencesRepresentation of informationUse casesElectronic health recordsReal-world evidence generationData scienceClinical textData modelClinical notesLanguage processingHealth recordsLoad dataClinical documentationCurrent applicationsInformationWorkflowEvidence generation
2022
Improving Pharmacovigilance Signal Detection from Clinical Notes with Locality Sensitive Neural Concept Embeddings.
Mower J, Bernstam E, Xu H, Myneni S, Subramanian D, Cohen T. Improving Pharmacovigilance Signal Detection from Clinical Notes with Locality Sensitive Neural Concept Embeddings. AMIA Joint Summits On Translational Science Proceedings 2022, 2022: 349-358. PMID: 35854716, PMCID: PMC9285153.Peer-Reviewed Original ResearchNatural language processingClinical notesRetrieval tasksConcept embeddingsNeural embeddingsLeverage informationLanguage processingEmbedding methodPharmacovigilance signal detectionADR signalsInherent complexityEmbeddingSignal detectionSignal recoveryAdverse drug reactionsStatistical measuresInformationDetection
2021
A Discrete Joint Model for Entity and Relation Extraction from Clinical Notes.
Ji Z, Ghiasvand O, Wu S, Xu H. A Discrete Joint Model for Entity and Relation Extraction from Clinical Notes. AMIA Joint Summits On Translational Science Proceedings 2021, 2021: 315-324. PMID: 34457146, PMCID: PMC8378610.Peer-Reviewed Original ResearchConceptsRelation classificationPipeline architectureClinical natural language processingNatural language processingEntity recognitionBeam searchRelation extractionClinical notesLanguage processingClassification stepEntity pairsStructured perceptronFundamental taskClinical narrativesTraditional solutionsRecognition stepError propagationArchitectureJoint modelTaskSubtasksPerceptronClinical conceptsEntitiesClassification
2020
Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus
Li Y, Luo Y, Wampfler J, Rubinstein S, Tiryaki F, Ashok K, Warner J, Xu H, Yang P. Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus. JCO Clinical Cancer Informatics 2020, 4: cci.19.00147. PMID: 32364754, PMCID: PMC7265793, DOI: 10.1200/cci.19.00147.Peer-Reviewed Original ResearchConceptsNatural language processing toolsElectronic health recordsLanguage processing toolsGold standard dataUnstructured electronic health recordsProcessing toolsAmount of dataClinical notesStandard dataMayo Clinic electronic health recordsClinic's electronic health recordEnvironment toolsAccurate annotationHealth recordsInformatics toolsEffective analysisData setsTextual sourcesCorpusToolInformationData extractionSetExtractingAnnotationA Natural Language Processing Tool to Extract Quantitative Smoking Status from Clinical Narratives
Yang X, Yang H, Lyu T, Yang S, Guo Y, Bian J, Xu H, Wu Y. A Natural Language Processing Tool to Extract Quantitative Smoking Status from Clinical Narratives. 2020 IEEE International Conference On Healthcare Informatics (ICHI) 2020, 00: 1-2. PMID: 33786419, PMCID: PMC8006894, DOI: 10.1109/ichi48887.2020.9374369.Peer-Reviewed Original Research
2019
Temporal indexing of medical entity in Chinese clinical notes
Liu Z, Wang X, Chen Q, Tang B, Xu H. Temporal indexing of medical entity in Chinese clinical notes. BMC Medical Informatics And Decision Making 2019, 19: 17. PMID: 30700331, PMCID: PMC6354334, DOI: 10.1186/s12911-019-0735-x.Peer-Reviewed Original ResearchConceptsSupport vector machineConvolutional neural networkTemporal indexingNeural network modelIndexing taskRelation classificationMedical entitiesRecurrent convolutional neural network modelMachine learning-based systemsConvolutional neural network modelDeep neural network modelNetwork methodNetwork modelLearning-based systemTemporal relation classificationRecurrent neural network methodChinese clinical notesTemporal relationsClinical notesNeural network methodI2b2 NLP challengeContext informationTime indexingSemantic informationBaseline methods
2018
Psychiatric stressor recognition from clinical notes to reveal association with suicide
Zhang Y, Zhang O, Li R, Flores A, Selek S, Zhang X, Xu H. Psychiatric stressor recognition from clinical notes to reveal association with suicide. Health Informatics Journal 2018, 25: 1846-1862. PMID: 30328378, DOI: 10.1177/1460458218796598.Peer-Reviewed Original ResearchConceptsElectronic health recordsSuicidal behaviorHealth recordsSuicide ideation/attemptsTremendous economic burdenPsychiatric stressorsSuicide risk factorsRisk factorsEconomic burdenPsychiatric stressClinical notesLarge-scale studiesPsychiatric notesSuicideAssociationSignificant stressorsStressorsPrior studiesPercentPrevious studiesStudyLeveraging existing corpora for de-identification of psychiatric notes using domain adaptation.
Lee H, Zhang Y, Roberts K, Xu H. Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation. AMIA Annual Symposium Proceedings 2018, 2017: 1070-1079. PMID: 29854175, PMCID: PMC5977650.Peer-Reviewed Original Research
2017
A Pilot Study of Mining Association Between Psychiatric Stressors and Symptoms in Tweets
Du J, Zhang Y, Tao C, Xu H. A Pilot Study of Mining Association Between Psychiatric Stressors and Symptoms in Tweets. 2017, 1254-1257. DOI: 10.1109/bibm.2017.8217838.Peer-Reviewed Original ResearchInterweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes
Zhang O, Zhang Y, Xu J, Roberts K, Zhang X, Xu H. Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes. Lecture Notes In Computer Science 2017, 10351: 396-406. DOI: 10.1007/978-3-319-60045-1_41.Peer-Reviewed Original ResearchNatural language processing systemsWord representation featuresPsychiatric stressorsLanguage processing systemDeep learningDomain knowledgeElectronic health recordsUnsupervised learningInexact matchingClinical notesF-measureRepresentation featuresProcessing systemHealth recordsPsychiatric notesImportant problemMultiple sourcesExperimental resultsLearningAlgorithmChallengesMatchingNarrative textStressor dataRecall
2015
Parsing clinical text: how good are the state-of-the-art parsers?
Jiang M, Huang Y, Fan J, Tang B, Denny J, Xu H. Parsing clinical text: how good are the state-of-the-art parsers? BMC Medical Informatics And Decision Making 2015, 15: s2. PMID: 26045009, PMCID: PMC4460747, DOI: 10.1186/1472-6947-15-s1-s2.Peer-Reviewed Original Research
2014
Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.
Chen Y, Wrenn J, Xu H, Spickard A, Habermann R, Powers J, Denny J. Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies. AMIA Annual Symposium Proceedings 2014, 2014: 375-84. PMID: 25954341, PMCID: PMC4419906.Peer-Reviewed Original ResearchConceptsSelf-care capacityPalliative careClinical notesClinical exposureHealth care professionalsGait disordersMedication managementHospital careCare professionalsStudents' clinical exposureGeriatric competenciesBehavioral disordersAmerican AssociationCareDisordersCompetency domainsExposurePreliminary studyMedical studentsChapter 12 Linking Genomic and Clinical Data for Discovery and Personalized Care
Denny J, Xu H. Chapter 12 Linking Genomic and Clinical Data for Discovery and Personalized Care. 2014, 395-424. DOI: 10.1016/b978-0-12-401678-1.00012-9.Peer-Reviewed Original ResearchElectronic health recordsEHR dataNatural language processingSuch algorithmsLanguage processingDecision supportPhenotype algorithmsIdeal repositoryHealth recordsNumber of challengesRepositoryAlgorithmClinical notesClinical careClinical documentationGenomic dataResult dataAccurate caseDNA biobanksEarly demonstration projectsHealth care qualityClinical recordsMedication recordsClinical dataTool
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 termsComputingAccuracy
2011
Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases.
Xu H, Fu Z, Shah A, Chen Y, Peterson N, Chen Q, Mani S, Levy M, Dai Q, Denny J. Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. AMIA Annual Symposium Proceedings 2011, 2011: 1564-72. PMID: 22195222, PMCID: PMC3243156.Peer-Reviewed Original ResearchConceptsEntire electronic health recordElectronic health recordsNatural language processingHealth recordsStructured EHR dataMachine learningText dataNarrative text dataF-measureLanguage processingClinical narrativesEHR dataSuch tasksColorectal cancerDetection methodConcept identificationCohort of patientsColorectal cancer casesVanderbilt University HospitalCase detection methodsClinical notesCRC patientsCRC casesUniversity HospitalCancer casesData from clinical notes: a perspective on the tension between structure and flexible documentation
Rosenbloom S, Denny J, Xu H, Lorenzi N, Stead W, Johnson K. Data from clinical notes: a perspective on the tension between structure and flexible documentation. Journal Of The American Medical Informatics Association 2011, 18: 181-186. PMID: 21233086, PMCID: PMC3116264, DOI: 10.1136/jamia.2010.007237.Peer-Reviewed Original ResearchConceptsReusable dataElectronic health record system adoptionStructured documentationComputer-based documentation systemsClinical notesClinical documentationStructured dataText processingSystem adoptionRecord systemSuch systemsDocumentation systemWorkflowContent needsProvidersUsabilityDocumentationExpressivitySystemHealthcare providersPatient careDataProcessingMajor goalAdoption