Efficient Statistical Learning Methods for Personalized Medicine Using Large Scale Biomedical Data

使用大规模生物医学数据进行个性化医疗的高效统计学习方法

• 批准号: 9891071
• 负责人: Yuanjia Wang
• 金额: $ 32.89万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9891071
• 关键词: Academic Medical Centers; Address; Adverse event; Algorithms; Benefits and Risks; Categories; Center for Translational Science Activities; Classification; Clinical; Collaborations; Collection; Complement; Complex; Computer software; Data; Data Collection; Databases; Decision Making; Dependence; Dimensions; Documentation; Electronic Health Record; Ensure; Equilibrium; Exclusion Criteria; Formulation; Gaussian model; Goals; Health; Health Status; Healthcare; Healthcare Systems; Heterogeneity; Indiana; Informatics; International; Knowledge; Laboratories; Learning; Length; Measurement; Measures; Medical; Methodology; Methods; Modeling; Nature; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Pharmaceutical Preparations; Process; Quality Control; Randomized Controlled Trials; Records; Research; Risk; Safety; Sampling; Structure; Testing; Time; Treatment Protocols; adaptive learning; adverse event risk; algorithmic methodologies; analytical tool; base; big biomedical data; clinical data warehouse; clinical decision-making; clinical encounter; clinical practice; comorbidity; complex data; data modeling; data space; design; evidence base; feature extraction; heterogenous data; individual patient; individualized medicine; knowledge base; learning strategy; machine learning method; novel; optimal treatments; outreach; patient population; personalized decision; personalized medicine; population based; scale up; statistical learning; temporal measurement; theories; treatment effect; treatment guidelines; treatment response; treatment strategy

Project Summary/Abstract Current medical treatment guidelines largely rely on data from randomized controlled trials that study average effects, which may be inadequate for making individualized decisions for real-world patients. Large-scale electronic health records (EHRs) data provide unprecedented opportunities to optimize personalized treatment strategies and generate evidence relevant to real-world patients. However, there are inherent challenges in the use of EHRs, including non-experimental nature of data collection processes, heterogeneous data types with complex dependencies, irregular measurement patterns, multiple dynamic treatment sequences, and the need to balance risk and benefit of treatments. Using two high-quality EHR databases, Columbia University Medical Center's clinical data warehouse and the Indiana Network for Patient Care database, and focusing on type 2 diabetes (T2D), this proposal will develop novel and scalable statistical learning approaches that overcome these challenges to discover optimal personalized treatment strategies for T2D from real-world patients. Specifically, under Aim 1, we will develop a unified framework to learn latent temporal processes for feature extraction and dynamic patient records representation. Our approach will accommodate large-scale variables of mixed types (continuous, binary, counts) measured at irregular intervals. They extract lower-dimensional components to reflect patients' dynamic health status, account for informative healthcare documentation processes, and characterize similarities between patients. Under Aim 2, we will develop fast and efficient multi-category machine learning methods, in order to evaluate treatment propensities and adaptively learn optimal dynamic treatment regimens (DTRs) among the extensive number of treatment options observed in the EHRs. The methods will provide sequential decisions that determine the best treatment sequence for a T2D patient given his/her EHRs. Under Aim 3, we will develop statistical learning methods to assist multi-faceted treatment decision-making, which balances risks versus benefits when evaluating a DTR. Our approach will ensure maximizing benefit to the greatest extent while controlling all risk outcomes under the safety margins. For all aims, we will develop efficient stochastic resampling algorithms to scale up the optimization for massive data sizes. We will identify optimal DTRs for T2D using the extracted information from patients' comorbidity conditions, medications, and laboratory tests, as well as records-collection processes. Our methodologies will be applied and cross-validated between the two EHR databases. The treatment strategies learned from the representative EHR databases with a diverse patient population will be beneficial for individual patient care, assisting clinicians to adaptively choose the optimal treatment for a patient. Finally, we will disseminate our methods and results through freely available software and outreach to the informatics and clinical experts at our Centers for Translational Science and elsewhere.

项目总结/摘要 目前的医学治疗指南主要依赖于随机对照试验的数据， 平均效应，这可能不足以为现实世界的患者做出个性化决策。大规模 电子健康记录（EHR）数据为优化个性化治疗提供了前所未有的机会 策略并生成与现实世界患者相关的证据。然而，在这方面存在着固有的挑战。 使用EHR，包括数据收集过程的非实验性质，异构数据类型， 复杂的依赖性、不规则的测量模式、多个动态治疗序列，以及 以平衡治疗的风险和收益。使用两个高质量的电子健康记录数据库，哥伦比亚大学医学院 中心的临床数据仓库和印第安纳州患者护理网络数据库，并侧重于2型 糖尿病（T2 D），该提案将开发新的和可扩展的统计学习方法，克服这些 从现实世界的患者中发现T2 D的最佳个性化治疗策略的挑战。具体地说， 在目标1下，我们将开发一个统一的框架来学习用于特征提取的潜在时间过程， 动态患者记录表示。我们的方法将适应混合类型的大规模变量 （连续、二进制、计数）以不规则的间隔测量。他们提取低维成分来反映 患者的动态健康状况，说明信息性医疗保健文件编制过程，并表征 患者之间的相似性。在目标2下，我们将开发快速高效的多类别机器学习 方法，以评估治疗倾向和自适应学习最佳的动态治疗方案 （DTR）在EHR中观察到的大量治疗方案中。这些方法将提供 根据T2 D患者的EHR确定最佳治疗顺序的顺序决策。目标之下 3、我们将开发统计学习方法来辅助多方面的治疗决策， 评估DTR时的风险与收益。我们的做法将确保在最大程度上实现利益最大化 同时将所有风险结果控制在安全裕度之下。对于所有目标，我们将开发有效的随机 重新部署算法，以扩大对大规模数据大小的优化。我们将确定T2 D的最佳DTR 使用从患者的合并症、药物和实验室检查中提取的信息， as records记录collection收集processes处理.我们的方法将在两个EHR之间应用和交叉验证 数据库。从具有代表性的EHR数据库中了解到的治疗策略， 人口将有利于个别病人的护理，协助临床医生适应性地选择最佳治疗 为了一个病人最后，我们将通过免费软件和外联活动传播我们的方法和结果 我们的转化科学中心和其他地方的信息学和临床专家。