Administrative Supplement - Rapid Actionable Data for Opioid Response in Kentucky (RADOR-KY)
行政补充 - 肯塔基州阿片类药物反应的快速可操作数据 (RADOR-KY)
基本信息
- 批准号:10850016
- 负责人:
- 金额:$ 15.11万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-29 至 2025-09-29
- 项目状态:未结题
- 来源:
- 关键词:AddressAdministrative SupplementAffectAgreementAlgorithmsAreaArtificial IntelligenceAwarenessCaringClinicalCommunitiesCountyDataData LinkagesData SetData SourcesDevelopmentDisparateDisparity populationEmergency medical serviceEthicsEvaluationEventFundingGoalsHarm ReductionHumanInformaticsInterventionKentuckyLabelLinkLocationMachine LearningMeasurementMeasuresMissionModelingMonitorMorbidity - disease rateNatural Language ProcessingOpioidOutcomeOverdoseParentsPatient CarePatientsPerformancePharmaceutical PreparationsPlayPopulationPredictive AnalyticsPreventionProcessPublic HealthPublic Health PracticeRecordsReportingResearchResourcesRiskRoleRunningSafetyStatistical ModelsSystemSystematic BiasTimeTransportationUnited States National Institutes of HealthValidationVisualVisualizationVisualization softwareWorkalgorithmic biasartificial intelligence methodcomparativecomputerized data processingdata ingestiondesignhealth disparityimprovedinequitable distributionknowledge basemachine learning algorithmmachine learning modelmachine learning predictionmodel developmentmortalitymultiple data sourcesnovelopioid overdoseopioid use disorderoverdose preventionparent grantparent projectparitypopulation basedpredictive modelingresidenceresponsesocial health determinantstoolweb app
项目摘要
Abstract
Systematic and algorithmic biases in machine learning (ML) modeling and underlying definitions
for capturing opioid overdose may result in inaccuracies in burden measures for disparate groups,
potentially leading to an ineffective and unequal distribution of harm reduction and prevention
resources. Identifying and evaluating data and model biases and health disparities is critical to
effective public health practice and research. This project is a supplement to RADOR-KY (Rapid
Actionable Data for Opioid Response in Kentucky; 1-R01 DA057605-01). The RADOR-KY project
will build a robust state-wide surveillance system for opioid use disorder (OUD) including opioid
overdose, integrating multiple data sources to monitor and predict drug overdose mortality and
morbidity. The system will be used by stakeholders to inform data-driven action, supporting the
coordination and targeting of prevention and treatment efforts. As proposed in the parent grant
for this supplement, the RADOR-KY system will integrate several data sources, including
Emergency Medical Services (EMS) data, to develop machine learning predictive models and
forecasting for opioid overdoses to inform public health and public safety agencies’ actions and
planning. The proposed administrative supplement of RADR-KY will improve our understanding
of the ethical aspects of these machine learning/artificial intelligence methods. EMS run data for
opioid overdose surveillance is a promising new system that overcomes limitations of traditional
data sources, such as prolonged delays and omission of non-clinical overdose events. While
recent national standards have improved the structural components of EMS encounter data, the
quality and completeness of such data still necessitate reliance on patient care narratives for case
assertion. There have been a host of opioid overdose definitions proposed, typically focused on
keyword matches or other rule-based criteria, with little emphasis on definition validation,
comparative evaluations, or demographic parity. Critically, no previous models, whether machine
learning or rule-based, have considered demographic fairness in their approaches. Leveraging
our access to over 3.5 million EMS detailed encounter records access under RADOR-KY’s data
use agreement, along with expert-labeled and extracted data, we aim to assess these proposed
models against our own machine learning natural language processing classifier, particularly
considering disparate populations. The specific aims are to 1) Evaluate potential bias in the opioid
overdose data and definitions and identify suitable definitions for each specific sub-population;
and 2) Identify, address, and generate bias-aware ML-ready datasets.
摘要
项目成果
期刊论文数量(0)
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Svetla Stefanova Slavova其他文献
Svetla Stefanova Slavova的其他文献
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{{ truncateString('Svetla Stefanova Slavova', 18)}}的其他基金
Diversity Supplement - Rapid Actionable Data for Opioid Response in Kentucky (RADOR-KY)
多样性补充 - 肯塔基州阿片类药物反应的快速可操作数据 (RADOR-KY)
- 批准号:
10789054 - 财政年份:2022
- 资助金额:
$ 15.11万 - 项目类别:
Rapid Actionable Data for Opioid Response in Kentucky (RADOR-KY)
肯塔基州阿片类药物反应的快速可操作数据 (RADOR-KY)
- 批准号:
10588669 - 财政年份:2022
- 资助金额:
$ 15.11万 - 项目类别:
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