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， 疫情在美国。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。努力 卫生保健系统和支付者对抗阿片类药物流行病的障碍是缺乏准确和有效的 确定最有可能出现阿片类药物使用和过量问题的个人的方法， 这些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（如高风险）进行针对性干预（如药房锁定计划）。 阿片类药物剂量，使用传统的统计方法在先前的研究中确定。然而，这些传统 这些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， 多层次的互动和缺失的数据。此外，先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，机器学习是一种先进的技术， 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 的情况下，是上级优于那些使用传统方法开发。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的流行。 因此，拟议的研究将应用机器学习来开发预测算法， 使用以下数据源准确识别存在阿片类药物使用问题和过量的高风险患者： 方便支付者和医疗保健系统使用。该项目将建立在现有的学术国家 将新型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链接到电子 健康记录以捕获临床信息（例如，实验室结果、疼痛严重程度）在管理中不可用 数据，沿着死亡证明上关于致命过量的数据。这些数据涵盖2007-2016年，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，而不是仅基于索赔的方法来识别处于风险中的患者 阿片类药物滥用和过量使用的案例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。 鉴于精神健康/物质使用障碍（~50%）和阿片类药物使用（>20%）的高患病率， 在医疗补助登记者中，缺乏足够的预测算法，医疗补助是一个理想的环境， 拟议项目。这些分析将为合作的医疗补助计划提供有价值的信息， 他们可以应用于更精确的目标干预措施的工具，以防止有问题的阿片类药物使用和过量。