2024
Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing
Nargesi A, Adejumo P, Dhingra L, Rosand B, Hengartner A, Coppi A, Benigeri S, Sen S, Ahmad T, Nadkarni G, Lin Z, Ahmad F, Krumholz H, Khera R. Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing. JACC Heart Failure 2024 PMID: 39453355, DOI: 10.1016/j.jchf.2024.08.012.Peer-Reviewed Original ResearchReduced ejection fractionEjection fractionHeart failureLeft ventricular ejection fractionVentricular ejection fractionYale-New Haven HospitalIdentification of patientsCommunity hospitalIdentification of heart failureLanguage modelNorthwestern MedicineMeasure care qualityQuality of careNew Haven HospitalDeep learning-based natural language processingHFrEFGuideline-directed careDeep learning language modelsMIMIC-IIIDetect HFrEFNatural language processingReclassification improvementHospital dischargePatientsCare quality
2021
Cannabis use disorder among atrial fibrillation admissions, 2008–2018
Chouairi F, Miller PE, Guha A, Clarke J, Reinhardt SW, Ahmad T, Freeman JV, Desai NR, Friedman DJ. Cannabis use disorder among atrial fibrillation admissions, 2008–2018. Pacing And Clinical Electrophysiology 2021, 44: 1934-1938. PMID: 34506639, DOI: 10.1111/pace.14356.Peer-Reviewed Original ResearchConceptsAtrial fibrillation hospitalizationsAF hospitalizationsImpact of CUDNational Inpatient SampleProportion of admissionsDiseases diagnosis codesPrevalence of cannabisHistory of CUDHospital dischargeYounger patientsHigher proportionUnderserved patientsBlack raceDiagnosis codesInpatient SampleInternational ClassificationHospitalizationLegality of cannabisCannabis usePatientsAdmissionCodiagnosisLittle dataCUDCannabis