grant
Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant Bacteria
Organization STANFORD UNIVERSITYLocation STANFORD, UNITED STATESPosted 1 Jul 2023Deadline 30 Jun 2028
NIHUS FederalResearch GrantFY2025AccountingAdverse effectsAgreementAntibiotic AgentsAntibiotic DrugsAntibiotic ResistanceAntibiotic susceptibilityAntibioticsBacteriaBacteria resistanceBacteria resistantBacterial Antibiotic ResistanceBacterial InfectionsBacterial resistantBacteriuriaCenters for Disease ControlCenters for Disease Control and PreventionCenters for Disease Control and Prevention (U.S.)Cessation of lifeClinicalClinical DataClinical Decision Support SystemsClinical MicrobiologyClinical TrialsCollaborationsCollectionCombating Antibiotic Resistant BacteriaCommunitiesComputersConsultationsDataData BasesDatabasesDeathDiagnosticEMR systemElectronic Health RecordElectronicsEvaluationFAIR dataFAIR guiding principlesFAIR principlesFeedbackFindable, Accessible, Interoperable and Re-usableFindable, Accessible, Interoperable, and ReusableGuidelinesHealthHealth Care CostsHealth CostsHospital AdmissionHospitalizationHumanIndividualInfectionLearningMachine LearningManualsMeasuresMethodsMiscellaneous AntibioticModelingModern ManNatural Language ProcessingOutcome MeasurePatientsPatternPhenotypePredispositionProcessProspective StudiesReal-Time SystemsRecommendationReference StandardsReproducibilityResearchResistanceResistance to antibioticsResistant to antibioticsRiskSelection BiasSiteSpecificitySusceptibilitySymptomsSystems IntegrationTest ResultTestingTimeTrainingTranslatingUnited States Centers for Disease ControlUnited States Centers for Disease Control and PreventionUrinary tract infectionUrinary tract infectious diseaseUrineValidationWorkanti-microbial resistant infectionantibiotic drug resistanceantibiotic resistantantibiotic resistant bacteriaantimicrobial resistant infectionapplication program interfaceapplication programming interfaceautomated algorithmautomatic algorithmbacteria infectionbacterial antibiotic resistantbacterial diseasebacterial resistancebacterial resistance to antibioticclinical decision supportcombatcomputer based predictionconsultationcostdata basedata collected in real worlddata harmonizationdata sharingdata standardizationdata standardselectronicelectronic data sharingelectronic deviceelectronic health care recordelectronic health medical recordelectronic health plan recordelectronic health registryelectronic medical health recordelectronic medical record systemelectronic medical systemexperienceharmonized dataimprovedindividualized predictionsinnovateinnovationinnovativeintegrated systemmachine based learningmachine learning based methodmachine learning based modelmachine learning methodmachine learning methodologiesmachine learning modelmeasurable outcomemicrobialmodel developmentmodel developmentsnatural language understandingnoveloutcome measurementpersonalized predictionsphenotyping algorithmpoint of carepredictive modelingprospectiveprototypereal world datarealtime systemsresistance to Bacteriaresistance to Bacterialresistantresistant to Bacteriaresistant to Bacterialroutine carestatistical learningstatisticssystem integrationtooltreatment riskurinary infectionvalidations
Description preview
Project Summary: Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant Bacteria
Antimicrobial resistant infections already cause over 2.8 million illnesses and 24,000 deaths per year in
the US alone. The Centers for Disease Control and Prevention (CDC) identify antibiotic prescribing
stewardship as the most important action to…
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