grant
Developing Data-Driven Clinical Signatures for People Who Experience Hallucinations
Organization UNIVERSITY OF WASHINGTONLocation SEATTLE, UNITED STATESPosted 1 Apr 2024Deadline 31 Jan 2029
NIHUS FederalResearch GrantFY2026AddressAdoptedAgeAreaBehavioralCell PhoneCellular PhoneCellular TelephoneChronic CareClinicClinicalCommunitiesComplexComputer ModelsComputerized ModelsComputing MethodologiesDataData CollectionData Coordinating CenterData Coordination CenterData SetDetectionDevelopmentDiagnosticDictionaryEcological momentary assessmentElectronic Health RecordEmergency medical serviceEnvironmentEpidemiological dataEpidemiology dataEvaluationFAIR dataFAIR guiding principlesFAIR principlesFindable, Accessible, Interoperable and Re-usableFindable, Accessible, Interoperable, and ReusableFunctional impairmentGene TranscriptionGeneral PopulationGeneral PublicGenetic TranscriptionHallucinationsHospital AdmissionHospitalizationIndividualIndividual DifferencesIntensive CareInterviewLanguageMachine LearningMeasurementMeasuresMemoryMental Health ServicesMental Hygiene ServicesMental disordersMental health disordersMetadataMethodologyMethodsMobile PhonesModelingNatural Language ProcessingOutcomePaintParticipantPatient Self-ReportPatternPersonsPreventionProcessPsychiatric DiseasePsychiatric DisorderRNA ExpressionRaceRacesReportingResearch ResourcesResource AllocationResourcesRiskRisk FactorsSamplingSecureSelf-ReportSpeechStructureSubgroupSuicideSuicide attemptSymptomsSystemTechniquesTechnologyTestingTextTimeTrainingTranscriptTranscriptionagesbehavior measurementbehavioral measurebehavioral measurementclinical decision-makingclinical outcome measuresclinical relevanceclinically relevantcohortcomputational methodologycomputational methodscomputational modelingcomputational modelscomputer based methodcomputer based modelscomputer methodscomputerized modelingcomputing methoddensitydevelopmentaldiariesdigital dataelectronic health care recordelectronic health medical recordelectronic health plan recordelectronic health registryelectronic medical health recordemergency serviceepidemiologic dataexperiencefatal attemptfatal suicidefeature extractioniPhoneimprovedindexinginnovateinnovationinnovativeintent to dielexicalmachine based learningmachine learning based modelmachine learning modelmental health caremental illnessmeta datamultidisciplinarynatural language understandingneuralnon fatal attemptnonfatal attemptnovelparticipant engagementpatient engagementphenomenological modelsphenomenologyprospectivepsychiatric illnesspsychological disorderpsychoticracialracial backgroundracial originrecruitresponsesexsmart phonesmartphonespeech recognitionsuicidal attemptsuicidal risksuicide risksuicidestoolverbal
Description preview
PROJECT SUMMARY
Hallucinations are prevalent in the context of a wide variety of mental disorders but also occur in approximately 10% of the general population. Hallucinatory experiences are readily identifiable by those experiencing them, but they are not always indicators of conditions that lead to serious negative outcomes such as…
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