Federated Automated Survey Tool (FAST)
Federated Automated Survey Tool (FAST)
批准号:
10382821
负责人:
Alec J Bateman
金额:
$30.15万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-15 至 2023-08-31
关键词:
Acute DiseaseAddressAdministratorAffectAgreementAnxietyAnxiety DisordersAreaAwarenessBehavioralBehavioral ModelCategoriesChronic DiseaseCognitiveCollaborationsCommunitiesComputer softwareComputersCoronavirusDataData CollectionDatabasesDecision MakingDictionaryDimensionsDiseaseDisease ProgressionDisease SurveillanceDrug usageElectronic MailEmotionalEnvironmentExcisionFutureGeographyGoalsGovernmentHealth PersonnelHealth SurveysHealth behaviorHealth systemHypertensionIndividualInfluenzaInstitutesInstitutionInternetInterventionKnowledgeLawsLogistic RegressionsLongitudinal SurveysMechanicsMental DepressionMetadataMethodologyMethodsModelingMonitorNoiseOntologyOutcomeParticipantPatternPhasePoliciesPolicy DevelopmentsPolicy MakerPopulationPrevalenceProbabilityProbability SamplesProcessPublic HealthQuestionnaire DesignsQuestionnairesResearch PersonnelRespondentRiskSamplingSchoolsSemanticsServicesSeveritiesSmall Business Innovation Research GrantSocial DesirabilitySocial NetworkSourceSpecificityStandardizationStressStudentsSubstance abuse problemSuicideSurveillance ProgramSurveysTechniquesTextTimeTobacco useTrainingTwitterUnited States National Institutes of HealthUniversitiesValidationVirginiaanalytical toolapplication programming interfacebaseclinical carecohortdesigndisorder controldrinkingelectronic dataexperiencegraphical user interfaceimplementation costimprovedinsightinterestintervention programnoveloperationpost interventionprogramsprospectiveresponsesocialsocial mediatext searchingtheoriestooltrait
中文摘要
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英文摘要
Summary/Abstract
Public health officials within both acute and chronic disease realms have relied predominantly on survey data
to gather information on disease prevalence, behavioral models, risk populations, risk probability, and disease
progression. Conventional surveys are subject to a number of known limitations, such as respondents' reluctance
to participate, social desirability biases, lag time between questionnaire design, data collection, and availability of
results, and intermittent coverage of important topics due to associated implementation costs. Further, disease
control experts and policy makers lack access to real-time data and efficient tools to provide contextual awareness
vis-à-vis surveys that are implemented for disease surveillance and program management. The implications of
not having a timely and broader understanding of the environment and community affects the representativeness
and demographic specificity of assessments and of the data used to drive policies and interventions.
The proposed Federated Automated Survey Tool (FAST) will be developed as a collaboration among Barron
Associates, Inc., George Mason University, and University of Virginia researchers. FAST will be an analytics
platform that can be used by public health officials, clinical care investigators, institutional administrators, and
others to more easily survey targeted cohorts regarding acute and chronic diseases (e.g., influenza, coronavirus,
high blood pressure, etc.) and other indicators (e.g., depression prevalence, tobacco use, substance abuse, etc.) by
harnessing social media (e.g., Twitter) or other web/electronic data. Based on both automated and tailorable
investigator inputs, the proposed FAST platform will facilitate the construction of appropriate interrogations of
social media and web data to yield prospective and longitudinal insights to answer user-initiated questions.
The FAST analytics platform will enable local, national, and worldwide surveys on geographically- and
demographically-targeted social media and web users based on their Tweets, posts, emails, search, and other web
data and metadata. The FAST platform will utilize sophisticated text analytics and novel survey construction and
analysis techniques. The survey results will then be analyzed automatically to gain insights and answer a diverse
set of questions regarding targeted geographic- and demographic-specific prevalence and severity estimates.
These can be one-off surveys, pre- and post-intervention surveys, or online, real-time, longitudinal surveys. As
an example of the latter, school administrators could track national or more localized (i.e., geo-tagged) student
social media posts in real time regarding issues such as drinking, drug use, stress, depression, or suicide, enabling
administrators to better tailor services offered to students and/or detect the need for interventions.
The FAST platform will employ a consolidated approach that makes it relatively easy for non-experts to
create, administer, and survey social network and electronic data of nearly any cohort. With FAST, the full
range of probability sampling techniques (e.g., simple random samples, stratified random samples, etc.) will be
available to end-users, along with the corresponding estimated variance and bound on the error of the estimate.
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会议论文
Robust Blink-based Communication System for Patients in Bed
-
批准号:8395624
-
项目类别:
-
资助金额:$18.17万
-
财政年份:2012
-
负责人:Alec J Bateman
-
依托单位:
A Blink-based Communication (BLINC) System for Patients in Bed
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批准号:8830598
-
项目类别:
-
资助金额:$32.26万
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财政年份:2012
-
负责人:Alec J Bateman
-
依托单位:
海外基金