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 groups
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 informationMedicationsExtracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing
Coleman B, Finch D, Wang R, Luther S, Heapy A, Brandt C, Lisi A. Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing. Applied Clinical Informatics 2023, 14: 600-608. PMID: 37164327, PMCID: PMC10411229, DOI: 10.1055/a-2091-1162.Peer-Reviewed Original ResearchMeSH KeywordsChiropracticCohort StudiesFemaleHumansNatural Language ProcessingPainQuality Indicators, Health CareQuality of Health CareVeterans HealthConceptsCare quality indicatorsVeterans Health AdministrationVisit typePain assessmentChiropractic careConsultation visitCare qualityClinic visit typePain care qualityComprehensive pain assessmentWomen Veterans Cohort StudyCare visitsCohort studyMusculoskeletal painManagement visitsVisit notesHealth AdministrationConsultation notesChiropractic servicesVisitsQuality indicatorsMean numberTreatment planningPatientsDescriptive statistics
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
Biomedical Informatics Techniques for Processing and Analyzing Web Blogs of Military Service Members
Konovalov S, Scotch M, Post L, Brandt C. Biomedical Informatics Techniques for Processing and Analyzing Web Blogs of Military Service Members. Journal Of Medical Internet Research 2010, 12: e45. PMID: 20923755, PMCID: PMC3234168, DOI: 10.2196/jmir.1538.Peer-Reviewed Original ResearchA 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