2023
Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV
Torgersen J, Akers S, Huo Y, Terry J, Carr J, Ruutiainen A, Skanderson M, Levin W, Lim J, Taddei T, So‐Armah K, Bhattacharya D, Rentsch C, Shen L, Carr R, Shinohara R, McClain M, Freiberg M, Justice A, Re V. Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV. Pharmacoepidemiology And Drug Safety 2023, 32: 1121-1130. PMID: 37276449, PMCID: PMC10527049, DOI: 10.1002/pds.5648.Peer-Reviewed Original ResearchMeSH KeywordsCross-Sectional StudiesDeep LearningFatty LiverHIV InfectionsHumansRetrospective StudiesTomography, X-Ray ComputedConceptsSevere hepatic steatosisHepatic steatosisHIV statusLiver attenuationHounsfield unitsPredictive valueRadiologist assessmentUS Veterans Health AdministrationNoncontrast abdominal CTVeterans Health AdministrationCross-sectional studySample of patientsNegative predictive valueReal-world studyPositive predictive valueAbdominal CTLiver fatTomography scanSteatosisCT imagesHealth AdministrationPharmacoepidemiologic studiesRadiologist reviewHIVPercent agreement
2022
Survival analysis of localized prostate cancer with deep learning
Dai X, Park JH, Yoo S, D’Imperio N, McMahon BH, Rentsch CT, Tate JP, Justice AC. Survival analysis of localized prostate cancer with deep learning. Scientific Reports 2022, 12: 17821. PMID: 36280773, PMCID: PMC9592586, DOI: 10.1038/s41598-022-22118-y.Peer-Reviewed Original ResearchMeSH KeywordsCross-Sectional StudiesDeep LearningHumansMaleProstate-Specific AntigenProstatic NeoplasmsSurvival AnalysisUnited StatesConceptsProstate cancer mortalityComposite outcomeCancer mortalityRisk predictionTime-dependent c-statisticsProstate-specific antigen (PSA) testLarge integrated healthcare systemLocalized prostate cancerElectronic health record dataClinical decision-making processProstate cancer patientsIntegrated healthcare systemProstate Cancer Risk PredictionHealth record dataLarge-scale electronic health record dataRisk prediction modelCancer risk predictionAntigen testC-statisticCancer patientsProstate cancerClinical decision systemSurvival analysisVeterans AffairsDeep learningPotentially inappropriate medication use by level of polypharmacy among US Veterans 49–64 and 65–70 years old
Guillot J, Rentsch CT, Gordon KS, Justice AC, Bezin J. Potentially inappropriate medication use by level of polypharmacy among US Veterans 49–64 and 65–70 years old. Pharmacoepidemiology And Drug Safety 2022, 31: 1056-1074. PMID: 35780391, PMCID: PMC9464694, DOI: 10.1002/pds.5506.Peer-Reviewed Original ResearchMeSH KeywordsCross-Sectional StudiesFemaleHumansInappropriate PrescribingMaleMiddle AgedPolypharmacyPotentially Inappropriate Medication ListPrevalenceRisk FactorsVeteransConceptsLevel of polypharmacyRace/ethnicityPIM prevalencePrevalence of PIMsInappropriate medication useElectronic health recordsCommon PIMsPharmacy fillsPROMPT criteriaInappropriate medicationsOlder patientsMedication usePsychotropic medicationsRefill recordsPolypharmacyPatientsVeterans AffairsMedicationsPrevalenceHealth recordsFiscal year 2016AgeMeaningful differencesSexTarget age