Philip Adejumo
About
Research
Publications
2025
A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model.
Zhou X, Dhingra L, Aminorroaya A, Adejumo P, Khera R. A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model. AMIA Annual Symposium Proceedings 2025, 2024: 1332-1339. PMID: 40417570.Peer-Reviewed Original ResearchConceptsCommon data modelElectronic health recordsOMOP Common Data ModelSchema mappingsMapping electronic health recordData modelTransformer-based deep learning modelsNatural language processing approachEnd-to-endDeep learning modelsHealth recordsEnhance interoperabilityTransformation pipelineLearning modelsOMOPProcessing approachSchemaStandard conceptsDiverse healthcare systemsInteroperabilityLarge-scaleStandard mapDatasetSoftwareHealthcare systemAutomated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models
Shankar S, Dhingra L, Aminorroaya A, Adejumo P, Nadkarni G, Xu H, Brandt C, Oikonomou E, Pedroso A, Khera R. Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models. European Heart Journal - Digital Health 2025, 6: 783-796. PMID: 40703108, PMCID: PMC12282380, DOI: 10.1093/ehjdh/ztaf030.Peer-Reviewed Original ResearchIMPROVING ATRIAL FIBRILLATION RISK ASSESSMENT WITH A MULTIMODAL RAG SYSTEM FOR COMPREHENSIVE CHADS-VASC AND HAS-BLED SCORING
Adejumo P, Thangaraj P, Shankar S, Camargos A, Aminorroaya A, Khera R. IMPROVING ATRIAL FIBRILLATION RISK ASSESSMENT WITH A MULTIMODAL RAG SYSTEM FOR COMPREHENSIVE CHADS-VASC AND HAS-BLED SCORING. Journal Of The American College Of Cardiology 2025, 85: 2851. DOI: 10.1016/s0735-1097(25)03335-2.Peer-Reviewed Original ResearchA FULLY AUTOMATED ELECTRONIC CLINICAL QUALITY MEASUREMENT FOR HEART FAILURE USING RETRIEVAL AUGMENTED GENERATION AND LARGE LANGUAGE MODELS (LLMS)
Adejumo P, Thangaraj P, Biswas D, Shankar S, Camargos A, Khera R. A FULLY AUTOMATED ELECTRONIC CLINICAL QUALITY MEASUREMENT FOR HEART FAILURE USING RETRIEVAL AUGMENTED GENERATION AND LARGE LANGUAGE MODELS (LLMS). Journal Of The American College Of Cardiology 2025, 85: 1372. DOI: 10.1016/s0735-1097(25)01856-x.Peer-Reviewed Original Research
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 ResearchConceptsFunctional 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 groupsAutomated 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, 13: 75-87. 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
2022
Characteristics of patients referred to a cardiovascular disease clinic for post-acute sequelae of SARS-CoV-2 infection
Wang SY, Adejumo P, See C, Onuma OK, Miller EJ, Spatz ES. Characteristics of patients referred to a cardiovascular disease clinic for post-acute sequelae of SARS-CoV-2 infection. American Heart Journal Plus Cardiology Research And Practice 2022, 18: 100176. PMID: 35856065, PMCID: PMC9277988, DOI: 10.1016/j.ahjo.2022.100176.Peer-Reviewed Original ResearchSARS-CoV-2 infectionCardiovascular disease clinicPost-acute sequelaeDisease clinicCommon symptomsExercise intoleranceChest pain/pressureNew cardiac diagnosesNew cardiovascular diseaseCharacteristics of patientsPain/pressurePattern of symptomsCOVID-19 diagnosisCardiovascular manifestationsCardiovascular symptomsCardiovascular diseaseDiagnostic evaluationObservational studyAverage agePatientsCardiac diagnosisCardiac pathologyCardiovascular diagnosisSymptomsDiagnosis
News
News
- November 05, 2024
Yale Researchers at American Heart Association Scientific Session 2024
- April 01, 2024
Yale Faculty Present Groundbreaking Clinical Research at the 2024 American College of Cardiology Scientific Sessions
- November 01, 2022Source: American Heart Association News
Perceived discrimination increased the risk of worse health outcomes after a heart attack