2024
134 Race and ethnicity-related differences in the diagnosis of heart failure with preserved ejection fraction using natural language processing
Brown S, Biswas D, Wu H, Ryan M, Bernstein B, Fairhurst N, Kaye G, Baral R, Cannata A, Searle T, Melikian N, Sado D, Lüscher T, Teo J, Dobson R, Bromage D, McDonagh T, Vazir A, Shah A, O’Gallagher K. 134 Race and ethnicity-related differences in the diagnosis of heart failure with preserved ejection fraction using natural language processing. 2024, a140-a141. DOI: 10.1136/heartjnl-2024-bcs.132.Peer-Reviewed Original ResearchNatural language processingArtificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction
Wu J, Biswas D, Ryan M, Bernstein B, Rizvi M, Fairhurst N, Kaye G, Baral R, Searle T, Melikian N, Sado D, Lüscher T, Grocott‐Mason R, Carr‐White G, Teo J, Dobson R, Bromage D, McDonagh T, Shah A, O'Gallagher K. Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction. European Journal Of Heart Failure 2024, 26: 302-310. PMID: 38152863, DOI: 10.1002/ejhf.3115.Peer-Reviewed Original ResearchConceptsLeft ventricular ejection fractionDiagnosis of HFpEFEuropean Society of CardiologyHeart failureNatural language processingElectronic health recordsEuropean Society of Cardiology criteriaClinical diagnosis of HFEuropean Society of Cardiology diagnostic criteriaDiagnostic criteriaVentricular ejection fractionRetrospective cohort studyDiagnosis of HFSociety of CardiologyClinician-assigned diagnosisConsecutive patientsHFpEF patientsEjection fractionElectronic health record dataAcute cardiovascular eventsExpert clinical reviewNatural language processing methodsNatural language processing pipelineHFpEFCardiovascular events
2023
Using natural language processing to generate a large-scale database of aortic stenosis with long-term follow-up: the CASPER (cogstack aortic stenosis patient electronic registry) database
Wu J, Biswas D, Seale T, Bean D, Fairhurst N, Kaye G, Dobson R, Chowienczyk P, Shah A, O'gallagher K. Using natural language processing to generate a large-scale database of aortic stenosis with long-term follow-up: the CASPER (cogstack aortic stenosis patient electronic registry) database. European Heart Journal 2023, 44: ehad655.2952. DOI: 10.1093/eurheartj/ehad655.2952.Peer-Reviewed Original ResearchNatural language processingElectronic heath recordsLanguage processingData pipelineNatural language processing toolkitRetrieval systemProcessing toolkitSNOMED termsClinical notesAutomated detectionRandomised controlled trialsManual validationLong-term mortality dataCohort of AS patientsMortality dataData sourcesSocial deprivationPatient trajectoriesInfluence of ethnicityControlled trialsPatient demographic dataRandomised trialsExtract dataDatabaseDemographic data