Administrative Supplement - Rapid Actionable Data for Opioid Response in Kentucky (RADOR-KY)

摘要 机器学习（ML）建模和基础定义中的系统和算法偏差 用于捕获阿片类药物过量可能导致不同群体的负担测量不准确， 可能导致减少危害和预防的无效和不平等分布 资源识别和评估数据和模型偏差以及健康差异至关重要， 有效的公共卫生实践和研究。本项目是RADOR-KY（Rapid 肯塔基州阿片类药物反应的可操作数据; 1-R 01 DA 057605 -01）。RADOR-KY项目 将建立一个强大的全州阿片类药物使用障碍（OUD）监测系统，包括阿片类药物 药物过量，整合多个数据源，以监测和预测药物过量死亡率， 发病率利益攸关方将利用该系统为数据驱动的行动提供信息， 预防和治疗工作的协调和针对性。根据父母补助金的建议， 对于这一补充，RADOR-KY系统将集成几个数据源，包括 紧急医疗服务（EMS）数据，以开发机器学习预测模型， 预测阿片类药物过量，以告知公共卫生和公共安全机构的行动， 规划拟议的RADR-KY行政补充将提高我们的理解 这些机器学习/人工智能方法的伦理方面。EMS运行数据 阿片类药物过量监测是一种有前途的新系统，它克服了传统监测系统的局限性。 数据来源，如长期延迟和遗漏非临床用药过量事件。而 最近的国家标准已经改进了EMS遭遇数据的结构部件， 这些数据的质量和完整性仍然需要依赖于病例的患者护理叙述 断言。已经提出了一系列阿片类药物过量的定义，通常集中在 关键字匹配或其他基于规则的标准，很少强调定义验证， 比较评价或人口均等。关键是，没有以前的型号，无论是机器 学习或基于规则的，在他们的方法中考虑了人口公平。利用 我们访问了超过350万EMS详细的遭遇记录， 使用协议，沿着与专家标记和提取的数据，我们的目标是评估这些建议 模型与我们自己的机器学习自然语言处理分类器，特别是 考虑到不同的人群。具体目的是：1）评价阿片类药物的潜在偏倚 过量数据和定义，并确定每个特定亚群的适当定义; 以及2）识别、寻址和生成偏置感知ML就绪数据集。

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.