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

使用机器学习来预测有问题的处方阿片类药物使用和阿片类药物过量

• 批准号: 9421755
• 负责人: Walid F. Gellad
• 金额: $ 60.11万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9421755
• 关键词: Accident and Emergency department; Address; Adult; Alcohol or Other Drugs use; Algorithms; American; Arizona; Cessation of life; Characteristics; Classification; Clinical; Clinical Data; Complex; Data; Data Set; Data Sources; Death Certificates; Detection; Dose; Electronic Health Record; Emergency department visit; Ensure; Epidemic; Fee-for-Service Plans; Fraud; Genomics; Health system; Healthcare Systems; High Prevalence; Hospitals; Individual; Injury; Inpatients; Intervention; Letters; Link; Logistic Regressions; Machine Learning; Managed Care; Medicaid; Mental Health; Mental disorders; Methods; Modeling; Morphine; Opioid; Outcome; Overdose; Pain; Patients; Pattern; Pennsylvania; Pharmaceutical Preparations; Pharmacy facility; Preclinical Drug Evaluation; Prevalence; Reporting; Research; Risk; Risk Factors; Severities; Statistical Methods; Statistical Models; Subgroup; Substance Use Disorder; Techniques; Time; Trees; United States; United States Centers for Medicare and Medicaid Services; Urine; Work; base; beneficiary; cancer genomics; clinical predictors; combat; design; dosage; forest; high risk; innovation; learning strategy; milligram; model building; nonmedical use; novel; opioid abuse; opioid use; overdose death; prediction algorithm; prescription opioid; prescription opioid misuse; prevent; programs; service utilization; tool

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.

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， 美国的疫情。从 1999 年到 2015 年，处方阿片类药物过量死亡人数增加了四倍多。 由于缺乏准确和有效的信息，医疗保健系统和付款人对抗阿片类药物流行病的工作受到阻碍 确定最有可能阿片类药物使用和过量使用风险的个人的方法，从而导致广泛的 干预措施对患者来说是负担，对支付者来说是昂贵的。付款人目前正在定义高风险 并根据个人风险因素（例如高风险因素）采取有针对性的干预措施（例如药房锁定计划） 阿片类药物剂量，在先前的研究中使用传统统计方法确定。然而，这些传统 方法有很大的局限性，特别是在处理具有大量变量的大型数据集时， 多层次交互和缺失数据。此外，先前的研究侧重于识别风险因素而不是 而非预测实际风险。或者，机器学习是一种处理复杂问题的先进技术 大数据中的交互，揭示隐藏的模式，并产生精确的预测算法，在许多 案例，优于使用传统方法开发的案例。机器学习广泛应用于活动中 从欺诈检测到癌症基因组学，但尚未应用于解决阿片类药物流行问题。 因此，拟议的研究将应用机器学习来开发预测算法，该算法可以更多地 使用以下数据源准确识别阿片类药物使用和过量使用高风险的患者 付款人和医疗保健系统随时可以使用。该项目将建立在现有学术状态的基础上 合作伙伴关系将新颖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数据连接起来 用于捕获临床信息（例如实验室结果、疼痛严重程度）的健康记录在管理中不可用 数据，以及致命过量用药的死亡证明数据。这些数据涵盖 2007 年至 2016 年，将用于 实现两个具体目标：（1）开发并验证两种独立的预测算法来识别患者 阿片类药物使用问题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，而不是单独基于索赔的方法来识别面临风险的患者 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 和具有代表性分类树的 TreeNet，以及预测能力（例如误分类率） 这些算法将与传统的统计模型进行比较。 鉴于心理健康/物质使用障碍 (~50%) 和阿片类药物使用 (>20%) 的高患病率 在医疗补助参与者中，并且缺乏足够的预测算法，医疗补助是一个理想的环境 拟议的项目。这些分析将为合作医疗补助计划提供有价值的信息和 他们可以将这些工具应用于更精确的目标干预措施，以防止出现问题的阿片类药物使用和过量。