2023
Developing 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 reportsAlgorithmBladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence
Moore N, McWilliam A, Aneja S. Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Seminars In Radiation Oncology 2023, 33: 70-75. PMID: 36517196, DOI: 10.1016/j.semradonc.2022.10.009.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceHumansPrognosisRadiation OncologyReproducibility of ResultsUrinary Bladder NeoplasmsConceptsArtificial intelligenceMachine learningReliability of algorithmAccurate predictive modelsEfficient creationIntelligenceBladder cancer patientsRadiation oncology patientsAlgorithmPrognostic modellingRoutine clinical useClinical outcomesOncology patientsClinical recordsCancer patientsBladder cancerPredictive modelTreatment planClinical useMultiple treatment plansClinical implementationNext stepRadiation oncologyTreatment planningInterpretability
2019
Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.
Kann BH, Hicks DF, Payabvash S, Mahajan A, Du J, Gupta V, Park HS, Yu JB, Yarbrough WG, Burtness BA, Husain ZA, Aneja S. Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma. Journal Of Clinical Oncology 2019, 38: 1304-1311. PMID: 31815574, DOI: 10.1200/jco.19.02031.Peer-Reviewed Original ResearchConceptsNeck squamous cell carcinomaExtranodal extensionSquamous cell carcinomaLymph nodesCell carcinomaContrast-enhanced CT scanDiagnostic abilityBoard-certified neuroradiologistsTreatment escalationCancer Genome AtlasPathologic confirmationPretreatment identificationDiagnostic challengeExternal validation data setsPathology resultsPretreatment imagingPoor prognosticatorClinical utilityCT scanPatientsClinical decisionHNSCCDiagnostic accuracyInstitutional ValidationGenome Atlas