Validation of an Electronic Health Record–Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding
Shung D, Chan C, You K, Nakamura S, Saarinen T, Zheng N, Simonov M, Li D, Tsay C, Kawamura Y, Shen M, Hsiao A, Sekhon J, Laine L. Validation of an Electronic Health Record–Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding. Gastroenterology 2024, 167: 1198-1212. PMID: 38971198, PMCID: PMC11493512, DOI: 10.1053/j.gastro.2024.06.030.Peer-Reviewed Original ResearchElectronic health recordsGlasgow-Blatchford scoreEmergency departmentVery-low-risk patientsRisk scoreOakland scoreMachine learning modelsStructured data fieldsClinical risk scoreGastrointestinal bleedingAll-Cause MortalityHealth recordsLearning modelsManual data entrySecondary analysisRisk stratification scoresAssess proportionRed blood-cell transfusionPrimary outcomeProportion of patientsData entryOvert gastrointestinal bleedingPrimary analysisReceiver-operating-characteristic curveVery-low-riskArtificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging
Feher A, Bednarski B, Miller R, Shanbhag A, Lemley M, Miras L, Sinusas A, Miller E, Slomka P. Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2024, 65: jnumed.123.266761. PMID: 38548351, PMCID: PMC11064832, DOI: 10.2967/jnumed.123.266761.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingHF hospitalizationHeart failureStress left ventricular ejection fractionPerfusion imagingHF exacerbationPredictive of HF hospitalizationsSPECT/CT myocardial perfusion imagingMyocardial perfusionInternational cohortAcute heart failure exacerbationMedian Follow-UpVentricular ejection fractionReceiver-operating-characteristic curveClinical risk factorsHeart failure exacerbationExternal validation cohortAcute HF exacerbationPrevent HF hospitalizationsImaging parametersCalcium scoreEjection fractionClinical parametersCT scanValidation cohortImpact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning–Generated Biomarkers
Haider S, Zeevi T, Sharaf K, Gross M, Mahajan A, Kann B, Judson B, Prasad M, Burtness B, Aboian M, Canis M, Reichel C, Baumeister P, Payabvash S. Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning–Generated Biomarkers. Journal Of Nuclear Medicine 2024, 65: jnumed.123.266637. PMID: 38514087, PMCID: PMC11927063, DOI: 10.2967/jnumed.123.266637.Peer-Reviewed Original ResearchOropharyngeal squamous cell carcinomaSquamous cell carcinomaHuman papillomavirusRadiomic featuresIntraclass correlation coefficientCell carcinomaLentiform nucleusHuman papillomavirus statusReceiver-operating-characteristic analysisReceiver-operating-characteristic curvePET radiomic featuresUnivariate logistic regressionF-FDGPrimary tumorTraining cohortValidation cohortRadiomic biomarkersUnivariate analysisInterindividual comparabilityPredictive valueDegree of reproducibilityMedian areaRadiomic markersLogistic regressionAUC
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