2022
In with the old, in with the new: machine learning for time to event biomedical research
Danciu I, Agasthya G, Tate JP, Chandra-Shekar M, Goethert I, Ovchinnikova OS, McMahon BH, Justice AC. In with the old, in with the new: machine learning for time to event biomedical research. Journal Of The American Medical Informatics Association 2022, 29: 1737-1743. PMID: 35920306, PMCID: PMC9471708, DOI: 10.1093/jamia/ocac106.Peer-Reviewed Original Research
2021
Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans
Gerlovin H, Posner DC, Ho YL, Rentsch CT, Tate JP, King JT, Kurgansky KE, Danciu I, Costa L, Linares FA, Goethert ID, Jacobson DA, Freiberg MS, Begoli E, Muralidhar S, Ramoni RB, Tourassi G, Gaziano JM, Justice AC, Gagnon DR, Cho K. Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans. American Journal Of Epidemiology 2021, 190: 2405-2419. PMID: 34165150, PMCID: PMC8384407, DOI: 10.1093/aje/kwab183.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAnti-Bacterial AgentsAzithromycinCOVID-19COVID-19 Drug TreatmentDrug Therapy, CombinationFemaleHospitalizationHumansHydroxychloroquineIntention to Treat AnalysisMachine LearningMaleMiddle AgedPharmacoepidemiologyRetrospective StudiesSARS-CoV-2Treatment OutcomeUnited StatesVeteransConceptsUS veteransCOVID-19Veterans Affairs Health Care SystemRecent randomized clinical trialsAdministration of hydroxychloroquineEffectiveness of hydroxychloroquineRisk of intubationEffect of hydroxychloroquineElectronic health record dataRandomized clinical trialsTreatment of patientsUS veteran populationCOVID-19 outcomesCoronavirus disease 2019Health record dataRigorous study designsHealth care systemSurvival benefitTreat analysisEarly therapyHospitalized populationClinical trialsObservational studyDisease 2019Hydroxychloroquine
2020
DNA methylation biomarker selected by an ensemble machine learning approach predicts mortality risk in an HIV-positive veteran population
Shu C, Justice AC, Zhang X, Marconi VC, Hancock DB, Johnson EO, Xu K. DNA methylation biomarker selected by an ensemble machine learning approach predicts mortality risk in an HIV-positive veteran population. Epigenetics 2020, 16: 741-753. PMID: 33092459, PMCID: PMC8216205, DOI: 10.1080/15592294.2020.1824097.Peer-Reviewed Original ResearchConceptsVeterans Aging Cohort StudyMortality risk groupsAging Cohort StudyHigher mortality riskMortality riskRisk groupsCohort studyHigh mortality risk groupLow-mortality risk groupsInflammation response pathwayHIV-positive participantsHuman immunodeficiency virusLow-risk groupImproved life expectancyVACS IndexHIV populationNatural killerImmunodeficiency virusPredictive biomarkersPLWHDNA methylation biomarkersVeteran populationSurvival analysisEpigenome-wide associationHIV
2018
Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality
Zhang X, Hu Y, Aouizerat BE, Peng G, Marconi VC, Corley MJ, Hulgan T, Bryant KJ, Zhao H, Krystal JH, Justice AC, Xu K. Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality. Clinical Epigenetics 2018, 10: 155. PMID: 30545403, PMCID: PMC6293604, DOI: 10.1186/s13148-018-0591-z.Peer-Reviewed Original ResearchConceptsWhite blood cellsSmoking-associated DNA methylationHIV prognosisInfection-related clinical outcomesBlood cellsSmoking-associated CpGsHIV-positive individualsImmune-related outcomesEpigenome-wide significant CpGsClinical outcomesTobacco smokingVeteran populationSurvival rateDNA methylation indexMortalityFrailtyHIVMethylation indexPrognosisMethylation signaturesDNA methylationOutcomesCell cycleCpGSignificant CpGs