2025
Detection of emergency department patients at risk of dementia through artificial intelligence
Cohen I, Taylor R, Xue H, Faustino I, Festa N, Brandt C, Gao E, Han L, Khasnavis S, Lai J, Mecca A, Sapre A, Young J, Zanchelli M, Hwang U. Detection of emergency department patients at risk of dementia through artificial intelligence. Alzheimer's & Dementia 2025, 21: e70334. PMID: 40457744, PMCID: PMC12130574, DOI: 10.1002/alz.70334.Peer-Reviewed Original ResearchConceptsElectronic health record dataHealth record dataEmergency departmentDetect dementiaDementia detectionYale New Haven HealthRecord dataRisk of dementiaEmergency department patientsBalance detection accuracyDementia algorithmsImprove patient outcomesCare coordinationCare transitionsDementia diagnosisReal-time applicationsClinical decision-makingClinician supportED usePatient safetyProbable dementiaMachine learning algorithmsED workflowED visitsED encountersIdentifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis
Coleman B, Corcoran K, Brandt C, Goulet J, Luther S, Lisi A. Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis. JMIR Medical Informatics 2025, 13: e66466. PMID: 40173367, PMCID: PMC12038758, DOI: 10.2196/66466.Peer-Reviewed Original ResearchMeSH KeywordsChiropracticDocumentationElectronic Health RecordsHumansMaleNatural Language ProcessingPatient Reported Outcome MeasuresUnited StatesUnited States Department of Veterans AffairsConceptsPatient-reported outcome measuresVeterans Health AdministrationChiropractic careNatural language processingPROM useNatural language processing approachCare quality dataCare quality metricsHealth care systemText notesText matchingClinical textHealth cohortVisit notesCare systemHealth AdministrationNatural language processing modelsMeasure documentationBag-of-wordsConvolutional neural networkOutcome measuresQuality improvementCareRule-based methodClinical notes
2024
Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure
Adejumo P, Thangaraj P, Dhingra L, Aminorroaya A, Zhou X, Brandt C, Xu H, Krumholz H, Khera R. Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure. JAMA Network Open 2024, 7: e2443925. PMID: 39509128, PMCID: PMC11544492, DOI: 10.1001/jamanetworkopen.2024.43925.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overConnecticutDeep LearningDocumentationElectronic Health RecordsFemaleFunctional StatusHeart FailureHumansMaleMiddle AgedNatural Language ProcessingROC CurveConceptsFunctional status assessmentArea under the receiver operating characteristic curveClinical documentationElectronic health record dataHF symptomsOptimal care deliveryHealth record dataAssess functional statusStatus assessmentClinical trial participationProcessing of clinical documentsFunctional status groupCare deliveryOutpatient careMain OutcomesMedical notesTrial participantsNew York Heart AssociationFunctional statusQuality improvementRecord dataHeart failureClinical notesDiagnostic studiesStatus groupsLeveraging Electronic Health Records to Assess Residential Mobility Among Veterans in the Veterans Health Administration
Wang K, Hendrickson Z, Miller M, Abel E, Skanderson M, Erdos J, Womack J, Brandt C, Desai M, Han L. Leveraging Electronic Health Records to Assess Residential Mobility Among Veterans in the Veterans Health Administration. Medical Care 2024, 62: 458-463. PMID: 38848139, DOI: 10.1097/mlr.0000000000002017.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedCross-Sectional StudiesElectronic Health RecordsFemaleHumansMaleMiddle AgedPopulation DynamicsUnited StatesUnited States Department of Veterans AffairsVeteransConceptsVeterans Health AdministrationElectronic health recordsResidential addressesHealth recordsHealth AdministrationLeveraging electronic health recordsInfluence health care utilizationVeterans Health Administration dataAssociations of sociodemographicsHealth care utilizationHealth care systemPatient's residential addressCross-sectional analysisGeneralized logistic regressionCare utilizationHealth systemResidential mobilitySubstance use disordersCare systemPatient's residenceLogistic regressionVeteransMultinomial outcomesHealthOdds
2023
Implementer report: ICD-10 code F44.5 review for functional seizure disorder
Ali S, Bornovski Y, Gopaul M, Galluzzo D, Goulet J, Argraves S, Jackson-Shaheed E, Cheung K, Brandt C, Altalib H. Implementer report: ICD-10 code F44.5 review for functional seizure disorder. BMJ Health & Care Informatics 2023, 30: e100746. PMID: 37730251, PMCID: PMC10514602, DOI: 10.1136/bmjhci-2023-100746.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsElectronic Health RecordsEpilepsyHumansInternational Classification of DiseasesNatural Language ProcessingConceptsPositive predictive valueSeizure disorderICD-10Single ICD codeEHR problem listElectronic health recordsChart reviewDiagnostic codesInternational ClassificationDiagnostic codingPredictive valueICD codesProblem listDiagnostic precisionHealth recordsDisordersNatural language processing algorithmEHR systemsLanguage processing algorithmReviewCliniciansDiseasePatient Dietary Supplements Use: Do Results from Natural Language Processing of Clinical Notes Agree with Survey Data?
Redd D, Workman T, Shao Y, Cheng Y, Tekle S, Garvin J, Brandt C, Zeng-Treitler Q. Patient Dietary Supplements Use: Do Results from Natural Language Processing of Clinical Notes Agree with Survey Data? Medical Sciences 2023, 11: 37. PMID: 37367736, PMCID: PMC10304046, DOI: 10.3390/medsci11020037.Peer-Reviewed Original ResearchMeSH KeywordsDietary SupplementsElectronic Health RecordsHumansNatural Language ProcessingSelf ReportConceptsSelf-reported supplement useSupplement useClinical notesStructured medical recordsUnstructured clinical notesPrescription medicationsClinical recordsMedical recordsPhysician guidanceDietary supplementsNatural language processing toolsPatientsNatural language processingFolic acidHealthcare facilitiesLanguage processing toolsSupplementsF1 scoreLanguage processingPotential interactionsNLP performanceProcessing toolsNLP extractionExtra informationMedications
2011
The Yale cTAKES extensions for document classification: architecture and application
Garla V, Re V, Dorey-Stein Z, Kidwai F, Scotch M, Womack J, Justice A, Brandt C. The Yale cTAKES extensions for document classification: architecture and application. Journal Of The American Medical Informatics Association 2011, 18: 614-620. PMID: 21622934, PMCID: PMC3168305, DOI: 10.1136/amiajnl-2011-000093.Peer-Reviewed Original ResearchConceptsDocument classificationFeature extractionProcessing systemKnowledge Extraction SystemDocument classification systemClinical Text AnalysisDocument classifierFeature representationRadiology reportsOpen sourceClinical textText analysisTechnical challengesClassificationExtraction systemClassification systemRepresentationClassifierSemanticsSystemArchitectureRetrievalExtractionSyntaxExtension
2010
A comparison of two approaches to text processing: facilitating chart reviews of radiology reports in electronic medical records.
Womack JA, Scotch M, Gibert C, Chapman W, Yin M, Justice AC, Brandt C. A comparison of two approaches to text processing: facilitating chart reviews of radiology reports in electronic medical records. Perspectives In Health Information Management Is 2010, 7: 1a. PMID: 21063542, PMCID: PMC2966352.Peer-Reviewed Original Research
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