Predicting Substance Use among Military Veterans with a Positive MST Screen: A Machine Learning Approach
Predicting Substance Use among Military Veterans with a Positive MST Screen: A Machine Learning Approach
批准号:
10458304
负责人:
SHANNON FORKUS
金额:
$0.25万
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:
2021-05-24 至 2023-05-23
关键词:
AddressAffectAlgorithmsAreaBig DataCessation of lifeClinicalCodeComplexDataData SetDetectionDevelopmentDistressEtiologyExposure toGoalsGrantHealthHealth behaviorImpairmentIndividualInstitutionInterventionInvestigationKnowledgeLegalLinkMachine LearningMaintenanceManuscriptsMapsMethodologyMilitary PersonnelMissionNational Institute of Drug AbuseNatureOccupationalOutcomeOutputOverdosePoliciesPopulationPopulation ResearchPreventionPrognosisPsychologyPublic HealthRecording of previous eventsResearchResearch MethodologyResearch PersonnelResearch Project GrantsRhode IslandRiskRisk FactorsSamplingScienceSelf MedicationService provisionSexual HarassmentStatistical Data InterpretationStatistical MethodsSuicideSymptomsTimeTo specifyTraffic accidentsTranslatingUnited States Department of Veterans AffairsUniversitiesVeteranscareerclinically relevantcohortdesignexperiencehigh riskhigh risk behaviorhigh risk populationimprovedinnovationlongitudinal analysislongitudinal datasetmachine learning methodmilitary servicemilitary veteranprediction algorithmpredictive modelingpreventive interventionprogramsprospectivesexual assaultsexual traumaskillssocialstemsubstance misusesubstance usesupervised learningtheoriestrauma exposure
中文摘要
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英文摘要
Project Summary/Abstract
Military sexual trauma (MST) is a serious and pervasive problem among military populations, affecting
approximately 16% of military personnel and veterans [1]. Substance use disproportionately affects individuals
with a history of MST. Individuals with (vs. without) a history of MST are twice as likely to misuse substances
[2-4]. Substance use among military samples has been linked to higher rates of negative consequences across
several domains (e.g., health, occupational, legal [5, 6]), including death (e.g., overdose [7], traffic accidents
[8], suicide [7, 8]). Further, while understudied among individuals with a history of MST in particular, negative
substance use outcomes have been shown to be more severe among trauma-exposed populations, including
more severe clinical presentations and poorer treatment prognosis [9, 10]. These findings emphasize the
importance of clarifying the association between MST and substance use among military populations.
Despite the clinical relevance and public health significance of substance use among military populations,
research in this area has relied almost exclusively on cross-sectional designs. Moreover, the vast majority of
studies in this area have utilized traditional statistical methods, which are limited in scope and capabilities.
These limitations have important clinical implications, as they restrict our ability to specify the exact nature and
directionality of the relationship between MST and substance use, thereby affecting how findings are translated
into prevention and intervention efforts. The proposed research aims to fill these critical gaps by utilizing the
Army STARRS pre/post-deployment study, a large, prospective military dataset to: (1) explicate the directional
relation between MST and substance use using a longitudinal dataset, and (2) employ machine learning
methods to develop an algorithm to optimize detection of substance use in military personnel with a history of
MST. These findings will assist in elucidating the etiology of substance use among this high-risk group, as well
as provide a prediction model for clinical use to better target at-risk individuals in this population.
This research project will take place within the Department of Psychology at the University of Rhode Island; an
institution with a strong history and commitment to health behavior research and methodology. The applicant
will have access to sponsors and consultants with expertise in MST, substance use, advanced methodology,
and statistical analysis that will facilitate her career objectives to develop increased knowledge and proficiency
in (a) sexual trauma (e.g., MST) and substance use in military veterans; (b) grant/manuscript development; (c)
statistical and methodological capabilities (i.e., machine learning); and (d) big data.
The proposed project uses a timely and innovative approach to advance science on the relation between MST
and substance use in military personnel. Addressing substance use in this population is necessary to improve
the health of our nation's veterans, and aligns with the mission of the National Institute on Drug Abuse.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
The effect of self-compassion on integrated treatment outcomes among veterans with co-occurring AUD and PTSD.
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批准号:10750544
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项目类别:
-
资助金额:$7.12万
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财政年份:2023
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负责人:SHANNON FORKUS
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依托单位:
Predicting Substance Use among Military Veterans with a Positive MST Screen: A Machine Learning Approach
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批准号:10404488
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项目类别:
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资助金额:$0.72万
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财政年份:2021
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负责人:SHANNON FORKUS
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依托单位:
海外基金