Using Machine Learning to Predict Problematic Prescription Opioid Use and Opioid Overdose

有问题的处方阿片类药物使用，定义为非医疗使用、误用或滥用阿片类药物， 在美国流行。从1999年到2015年，处方阿片类药物过量死亡人数增加了三倍多。努力工作： 抗击阿片类药物流行的卫生保健系统和付款人因缺乏准确和有效率而受阻 确定问题阿片类药物使用和过量使用风险最大的个人的方法，导致广泛的 干预措施给患者带来负担，对支付者来说也很昂贵。付款人目前正在定义高风险 以及基于个人风险因素的针对性干预措施(例如，药房锁定计划)，如高 阿片类药物剂量，在先前的研究中使用传统的统计方法确定。然而，这些传统的 方法有很大的局限性，特别是在处理具有许多变量的大型数据集时， 多层次的交互和缺失的数据。此外，以前的研究侧重于确定风险因素，而不是 而不是预测实际风险。或者，机器学习是一种处理复杂问题的高级技术 在大数据中的交互，发现隐藏的模式，并产生精确的预测算法，在许多情况下 案例，比用传统方法开发的要好。机器学习被广泛应用于活动中 从欺诈检测到癌症基因组学，但尚未被应用于解决阿片类药物的流行。 因此，这项拟议的研究将应用机器学习来开发预测算法，该算法可以 使用以下数据源准确识别有问题的阿片类药物使用和过量使用的高风险患者 支付者和医疗保健系统可以随时获得。该项目将建立在现有的学术状态基础上 合作伙伴将新的机器学习方法应用于所有医疗补助的行政索赔数据 宾夕法尼亚州(PA)和亚利桑那州(AZ)的受益人。该项目还将把亚利桑那州的医疗补助数据与电子产品联系起来 用于记录临床信息(例如，实验室结果、疼痛严重程度)的健康记录在管理部门中不可用 数据，以及致命过量的死亡证明数据。这些涵盖2007-2016年的数据将用于 实现两个具体目标：(1)开发和验证两个独立的预测算法，以识别患者 有问题的阿片类药物使用和阿片类药物过量的风险；(2)比较预测算法的准确性 将临床数据与医疗补助索赔相结合，而不是仅使用基于索赔的方法来识别有风险的患者 有问题的阿片类药物使用和阿片类药物过量。机器学习方法将包括随机森林 和TreeNet，具有代表性的分类树，以及预测能力(例如，误分率) 这些算法将与传统的统计模型进行比较。 鉴于精神健康/物质使用障碍(~50%)和阿片类药物使用(&gt；20%)的高流行率 在医疗补助计划的参与者中，由于缺乏足够的预测算法，医疗补助计划是 建议的项目。这些分析将为合作医疗补助计划提供有价值的信息和 他们可以应用于更准确地针对干预措施的工具，以防止有问题的阿片类药物使用和过量。

Problematic prescription opioid use, defined as nonmedical use, misuse, or abuse of opioid medications, is epidemic in the US. Prescription opioid overdose deaths more than quadrupled from 1999 to 2015. Efforts by health care systems and payers to combat the opioid epidemic are impeded by a lack of accurate and efficient methods to identify individuals most at risk for problematic opioid use and overdose, leading to broad interventions that are burdensome to patients and expensive for payers. Payers are currently defining high risk and targeting interventions (e.g. pharmacy lock-in programs) based on individual risk factors, such as high opioid dosage, identified in prior studies using traditional statistical approaches. However, these traditional approaches have significant limitations, especially when handling large datasets with numerous variables, multi-level interactions, and missing data. Moreover, the prior studies focused on identifying risk factors rather than predicting actual risk. Alternatively, machine learning is an advanced technique that handles complex interactions in large data, uncovers hidden patterns, and yields precise prediction algorithms that, in many cases, are superior to those developed using traditional methods. Machine learning is widely used in activities from fraud detection to cancer genomics, but has not yet been applied to address the opioid epidemic. Accordingly, the proposed study will apply machine learning to develop prediction algorithms that can more accurately identify patients at high risk of problematic opioid use and overdose using data sources that are readily available to payers and health care systems. The project will build on existing academic-state partnerships to apply novel machine learning approaches to administrative claims data for all Medicaid beneficiaries in Pennsylvania (PA) and Arizona (AZ). The project will also link Medicaid data in AZ to electronic health records to capture clinical information (e.g., lab results, pain severity) not available in administrative data, along with death certificate data on lethal overdose. These data, covering 2007-2016, will be used to achieve two specific aims: (1) to develop and validate two separate prediction algorithms to identify patients at risk of problematic opioid use and opioid overdose; (2) to compare the accuracy of a prediction algorithm that integrates clinical data with Medicaid claims versus a claims-based approach alone to identify patients at risk of problematic opioid use and opioid overdose. The machine learning approaches will include random forests and TreeNet with representative classification trees, and the predictive ability (e.g., misclassification rates) of these algorithms will be compared to traditional statistical models. Given the high prevalence of mental health/substance use disorders (~50%) and opioid utilization (>20%) among Medicaid enrollees and the lack of adequate prediction algorithms, Medicaid is an ideal setting for the proposed project. These analyses will provide the partnering Medicaid programs with valuable information and tools that they can apply to more precisely target interventions to prevent problematic opioid use and overdose.