grant

Individualized immunosuppression in organ transplant recipients based on real world evidence and artificial intelligence

Organization NEW YORK UNIVERSITY SCHOOL OF MEDICINELocation NEW YORK, UNITED STATESPosted 15 Sept 2025Deadline 31 Jul 2030
NIHUS FederalResearch GrantFY2025AI basedAI systemAcuteAddressAdoptedAdverse drug eventAdvertisingAgeAlgorithm DesignAlgorithmic DesignAlgorithmic EngineeringAlgorithmsAnti-Rejection TherapyArtificial IntelligenceBenchmarkingBenefits and RisksBest Practice AnalysisBlood GlucoseBlood SugarCancersCardiovascular DiseasesCessation of lifeCharacteristicsClinicClinicalClinical DataComputer ReasoningD-GlucoseDataData BasesData SetDatabasesDeathDevelopmentDextroseDiabetes MellitusDiagnosisDrugsElectronic Health RecordEquilibriumFeedbackFeverGlucoseHistoryHospitalsImmuneImmunesImmunosuppressionImmunosuppression EffectImmunosuppressive EffectImmunosuppressive TherapyIndividualIndividual DifferencesInfectionInterruptionIschemiaKidney GraftingKidney TransplantationKidney TransplantsKnowledgeLipidsMachine IntelligenceMalignant NeoplasmsMalignant TumorMeasuresMedicalMedical HistoryMedicationMethodologyMethodsModelingOutcomePatientsPerformancePersonal Medical HistoryPersonal Medical History EpidemiologyPersonal SatisfactionPersonalized medical approachPharmaceutical PreparationsPopulationPredicting RiskProceduresProtocolProtocols documentationProviderPyrexiaRecording of previous eventsRegimenRenal GraftingRenal TransplantationRenal TransplantsResearch ResourcesResourcesRiskSpecialtySteroid CompoundSteroidsSubgroupTestingTherapeutic immunosuppressionTimeTrainingTransplant RecipientsTransplantationWithdrawalagesalgorithm engineeringalgorithmic compositionartificial immunosuppressionartificial intelligence basedbalancebalance functionbenchmarkcardiovascular disorderclinical applicabilityclinical applicationclinical decision supportclinical decision-makingclinical phenotypeclinical practiceclinical relevanceclinically relevantco-morbidco-morbiditycohortcomorbiditycustomized therapycustomized treatmentdata basedata collected in real worlddata registrydevelopmentaldiabetesdrug/agentelectronic health care recordelectronic health medical recordelectronic health plan recordelectronic health registryelectronic medical health recordevidence baseexperiencefallsfebrilefebrisforecasting riskgraft failurehistoriesimmune suppressionimmune suppressive activityimmune suppressive functionimmunosuppressedimmunosuppression therapyimmunosuppressive activityimmunosuppressive functionimmunosuppressive responseimprovedindividualized approachindividualized clinical decisionindividualized decisionindividualized medicineindividualized patient treatmentindividualized therapeutic strategyindividualized therapyindividualized treatmentinfection riskkidney txlarge scale datalarge scale data setslarge scale datasetsmachine learned algorithmmachine learning algorithmmachine learning based algorithmmalignancymedical specialtiesmortalityneglectneoplasm/cancernovelonline apporgan transplant patientorgan transplant recipientpatient specific therapiespatient specific treatmentpersonalized approachpersonalized clinical decisionpersonalized data-driven decisionpersonalized decisionprecision approachpredict riskpredict riskspredicted riskpredicted riskspredicting riskspredictive riskpredicts riskpreventpreventingreal world datareal world evidencerisk predictionrisk predictionsside effectsuccesssupport toolstailored approachtailored medical treatmenttailored therapytailored treatmenttooltransplanttransplant centerstransplant patienttransplant registrytreatment effectunique treatmentusabilityweb appweb applicationweb based appweb based applicationwell-beingwellbeing
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PROJECT SUMMARY
Over 250,000 kidney transplant (KT) recipients depend on lifelong immunosuppressive therapy to prevent

rejection and graft failure. However, immunosuppression increases the risk of complications such as infections,

cancer, and cardiovascular disease (CVD), which together account for >70% of deaths in KT patients. To

optimize…

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