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Developing Data-Driven Clinical Signatures for People Who Experience Hallucinations

Organization UNIVERSITY OF WASHINGTONLocation SEATTLE, UNITED STATESPosted 1 Apr 2024Deadline 31 Jan 2026 โš ๏ธ
NIHUS FederalResearch GrantFY2025AddressAdoptedAgeAreaBehavioralCell 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

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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 asโ€ฆ

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Developing Data-Driven Clinical Signatures for People Who Experience Hallucinations โ€” UNIVERSITY OF WASHINGTON | UNITED | Dev Procure