grant
Predicting Morbidity and Mortality for HIV-Related Opportunistic Infections
Organization BETH ISRAEL DEACONESS MEDICAL CENTERLocation BOSTON, UNITED STATESPosted 5 Sept 2024Deadline 31 Aug 2029
NIHUS FederalResearch GrantFY2025AIDS Associated Opportunistic InfectionAIDS VirusAIDS opportunistic infectionsAIDS-Related Opportunistic InfectionsAIDS/HIVAcquired Immune Deficiency Syndrome VirusAcquired Immunodeficiency Syndrome VirusAddressAffectAlgorithmsAwardCaringClinicalCommunicable DiseasesCommunitiesComplementComplement ProteinsComputer ModelsComputerized ModelsComputing MethodologiesContinuity of CareContinuity of Patient CareContinuum of CareCost Effectiveness AnalysisDataData BasesData ScienceData SetDatabasesDeath RateElectronic Health RecordEpidemicEpidemiologyFailureFoundationsFutureGeographyGoalsHIVHIV-Related Opportunistic InfectionsHIV/AIDSHealth InsuranceHealth PolicyHigh Performance ComputingHospital AdmissionHospital MortalityHospitalizationHuman Immunodeficiency VirusesIn-house MortalitiesIncidenceIndividualInfectious DiseasesInfectious DisorderInhospital MortalityInpatientsInstitutionInsurance CarriersInsurance CoverageInsurance StatusInsurersInvestigatorsLAV-HTLV-IIILymphadenopathy-Associated VirusMachine LearningMedicaidMentorshipMethodsModelingMorbidityMorbidity - disease rateOpportunistic InfectionsOutcomePatternPersonsPoliciesPopulationPredicting RiskPublic HealthResearchResearch PersonnelResearchersRiskRisk FactorsRuralSamplingSeriesTestingTimeTrainingTranslatingUninsuredVariantVariationViralViral BurdenViral LoadViral Load resultVirus-HIVVulnerable PopulationsWorkaccess to health careaccessibility of health careaccessibility to health carecareercomparativecomplementationcomputational methodologycomputational methodscomputational modelingcomputational modelscomputer based methodcomputer based modelscomputer based predictioncomputer methodscomputerized modelingcomputing methodcostcost efficient analysiscost-effective analysisdata basedeath riskdeep learning based modeldeep learning modelelectronic health care recordelectronic health medical recordelectronic health plan recordelectronic health registryelectronic medical health recordepidemiologicepidemiologicalforecasting riskhealth care accesshealth care availabilityhealth care policyhealth care service accesshealth care service availabilityhealth insurance planhigh riskhigh-end computinghospitalization ratesimprovedimproved outcomeindividual patientinnovateinnovationinnovativelong short term memorymachine based learningmachine learning based methodmachine learning based modelmachine learning methodmachine learning methodologiesmachine learning modelmalleable riskmodifiable riskmortalitymortality ratemortality ratiomortality riskopen source toolopen source toolkitoutcome predictionpatient subclasspatient subclusterpatient subgroupspatient subpopulationspatient subsetspatient subtypespoor health outcomepredict riskpredict riskspredicted riskpredicted riskspredicting riskspredictive modelingpredictive riskpredicts riskreduced health outcomerisk predictionrisk prediction algorithmrisk prediction modelrisk predictionsrural arearural locationrural regiontooltrendunsupervised learningunsupervised machine learningvulnerable groupvulnerable individualvulnerable peopleworse health outcome
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PROJECT SUMMARY
This project uses machine learning to predict and improve outcomes for people with HIV (PWH). The research
has been refined based on Year 1 findings, which highlighted significant regional variations in opportunistic infection (OI) patterns from the National Inpatient Sample (NIS) dataset. The project will now incorporate a…
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