2023
Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record
Dhingra L, Shen M, Mangla A, Khera R. Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record. The American Journal Of Cardiology 2023, 203: 136-148. PMID: 37499593, PMCID: PMC10865722, DOI: 10.1016/j.amjcard.2023.06.104.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsData CollectionDelivery of Health CareElectronic Health RecordsInformation Storage and RetrievalMulticenter Studies as TopicNatural Language ProcessingConceptsElectronic health recordsData elementsUnstructured data streamsUnstructured data elementsNatural language processingCommon data modelHealth recordsStructured data elementsComputer visionUnstructured dataData streamsHeterogeneity challengesSeamless deliveryData modelLanguage processingData storageFree textClinical narrativesComputational phenotypesOngoing workPatient informationRapid innovationSpecific expertiseConfidentialityOngoing innovationClinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity
Zhang L, Khera R, Mortazavi B. Clinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083199, PMCID: PMC11007255, DOI: 10.1109/embc40787.2023.10340765.Peer-Reviewed Original ResearchDeveloping Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study
Bikdeli B, Lo Y, Khairani C, Bejjani A, Jimenez D, Barco S, Mahajan S, Caraballo C, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Wang Y, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Aneja S, Khera R, Goldhaber S, Zhou L, Monreal M, Krumholz H, Piazza G. Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study. Thrombosis And Haemostasis 2023, 123: 649-662. PMID: 36809777, PMCID: PMC11200175, DOI: 10.1055/a-2039-3222.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsElectronic Health RecordsHumansInternational Classification of DiseasesPredictive Value of TestsPulmonary EmbolismReproducibility of ResultsConceptsElectronic health recordsNLP algorithmNatural language processing toolsLanguage processing toolsPrincipal discharge diagnosisICD-10 codesDischarge diagnosisNLP toolsChart reviewHealth systemProcessing toolsYale New Haven Health SystemPatient identificationElectronic databasesHealth recordsData validationHigh-risk PEPulmonary Embolism ResearchSecondary discharge diagnosisIdentification of patientsManual chart reviewNegative predictive valueCodeRadiology reportsAlgorithm
2021
Electronic health record risk score provides earlier prognostication of clinical outcomes in patients admitted to the cardiac intensive care unit
Kunitomo Y, Thomas A, Chouairi F, Canavan ME, Kochar A, Khera R, Katz JN, Murphy C, Jentzer J, Ahmad T, Desai NR, Brennan J, Miller PE. Electronic health record risk score provides earlier prognostication of clinical outcomes in patients admitted to the cardiac intensive care unit. American Heart Journal 2021, 238: 85-88. PMID: 33891906, DOI: 10.1016/j.ahj.2021.04.004.Peer-Reviewed Original ResearchConceptsCardiac intensive care unitIntensive care unitRothman IndexCare unitRisk scoreModern cardiac intensive care unitSequential Organ Failure Assessment scoreOrgan Failure Assessment scoreElectronic health recordsCICU mortalityCICU patientsSOFA scoreCICU admissionClinical outcomesEarly prognosticationObservational studyPrognostic abilityAssessment scoresOutcome predictionHealth recordsGood calibrationSuperior discriminationPatientsAdmissionScores