2024
Illusory generalizability of clinical prediction models
Chekroud A, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, Kambeitz J, Corlett P, Koutsouleris N, Krumholz H, Krystal J, Paulus M. Illusory generalizability of clinical prediction models. Science 2024, 383: 164-167. PMID: 38207039, DOI: 10.1126/science.adg8538.Peer-Reviewed Original Research
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
2016
Cross-trial prediction of treatment outcome in depression: a machine learning approach
Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, Cannon TD, Krystal JH, Corlett PR. Cross-trial prediction of treatment outcome in depression: a machine learning approach. The Lancet Psychiatry 2016, 3: 243-250. PMID: 26803397, DOI: 10.1016/s2215-0366(15)00471-x.Peer-Reviewed Original ResearchConceptsTreatment outcomesTreatment groupsEscitalopram treatment groupSpecific antidepressantsPatient-reported dataSequenced Treatment AlternativesClinical trial dataIndependent clinical trialsClinical remissionSymptomatic remissionClinical trialsTreatment efficacyPatientsProspective identificationTreatment alternativesTrial dataDepressionRemissionAntidepressantsOutcomesGroupLevel 1CitalopramCohortClinicians
2011
Baseline trajectories of heavy drinking and their effects on postrandomization drinking in the COMBINE Study: empirically derived predictors of drinking outcomes during treatment
Gueorguieva R, Wu R, Donovan D, Rounsaville BJ, Couper D, Krystal JH, O’Malley S. Baseline trajectories of heavy drinking and their effects on postrandomization drinking in the COMBINE Study: empirically derived predictors of drinking outcomes during treatment. Alcohol 2011, 46: 121-131. PMID: 21925828, PMCID: PMC3266454, DOI: 10.1016/j.alcohol.2011.08.008.Peer-Reviewed Original ResearchConceptsDaily heavy drinkersFrequent heavy drinkersHeavy drinkersHeavy drinkingDrinking outcomesAlcohol-dependent patientsBehavioral intervention studyHeavy drinking trajectoriesSummary drinking measuresBaseline characteristicsActive treatmentSevere baselineCombined PharmacotherapiesWorse outcomesPharmacological interventionsCOMBINE StudyIntervention studiesPatientsTreatment factorsDrinkersOutcomesTrajectory membershipDrinking measuresTreatment effectsDrinking