Data Science Core

数据科学核心

• 批准号: 10487462
• 负责人: SCOTT L. ZEGER
• 金额: $ 17.56万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-09 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10487462
• 关键词: Address; Back; Bayesian Method; Center Core Grants; Clinical; Clinical Data; Clinical Trials; Clinical assessments; Collaborations; Computer software; Computerized Medical Record; Computing Methodologies; Consult; Data; Data Science; Data Science Core; Database Management Systems; Databases; Disease; Event; Foundations; Funding; Goals; Health; Healthcare Systems; Heterogeneity; Individual; Information Systems; Infrastructure; Institution; Intervention; Laboratories; Learning; Markov Chains; Measurement; Measures; Medicine; Methods; Modeling; Modernization; Myositis; Observational Study; Outcome; Patients; Persons; Phase; Physics; Population; Reproducibility; Research Design; Research Personnel; Resources; Rheumatism; Rheumatology; Science; Scleroderma; Secure; Statistical Data Interpretation; Statistical Methods; Subgroup; Techniques; Treatment Efficacy; Update; Visualization software; base; clinical center; clinical practice; clinically relevant; cloud based; cohort; collaborative environment; complex data; design; disorder subtype; experimental study; implementation trial; improved; individual patient; insight; longitudinal dataset; novel; open source; optimal treatments; patient subsets; precision medicine; programs; ranpirnase; tool; treatment choice; treatment effect; treatment response

PROJECT SUMMARY The broad goal of the Data Science Core (DSC) is to advance and apply modern data science methods to support the practice of precision medicine in rheumatology. We define precision medicine as discovering mechanistically anchored and clinically relevant disease subgroups for which optimal strategies can be followed. The precision medicine statistical methods we develop and apply will improve the clinical assessment of an individual's disease status, trajectory, and likely benefits of competing interventions and thereby benefit people living with rheumatic diseases. During the past funding period, a team led by P30 PI Antony Rosen and DSC Director Scott Zeger, designed and implemented the JH Precision Medicine Analytics Platform (PMAP), a cloud-based data system: (1) receives a nightly download of all JHM clinical data and projects the data into a secure clinical cohort database; (2) provides an environment for collaborative, modern statistical analyses; and (3) provides tools to visualize from within the Electronic Medical Record those analytic results relevant to a particular patient's decisions. PMAP has already been implemented in the Scleroderma and Myositis Center. During the coming period, PMAP will become active in each rheumatology Center of Excellence so that current clinical cohort databases will form the information foundation for the next phase of this P30 program. This novel infrastructure makes it possible for the DSC to develop and apply novel statistical tools that identify disease subgroups, for whom optimal treatments can be evaluated. In this way, JHM Rheumatology will discover the key data science tools required by a learning healthcare system. The Specific Aims of the Data Science Core are to: (1) provide study design and analysis support that enables investigators to generate, manage, analyze, and interpret complex data using modern statistical and computing methods; (2) develop and apply multivariate hierarchical models (MHMs) to longitudinal datasets to identify patient subsets predictive of disease trajectories and major clinical events; and (3) apply modern statistical analytic approaches to observational and experimental studies that rigorously estimate treatment efficacy and safely, acknowledging the likely heterogeneity among individuals in their responses to treatments and the potential biases inherent in using observational data to address causal questions.

项目摘要 数据科学核心（DSC）的广泛目标是推进和应用现代数据科学 方法，以支持在风湿病学的精准医学实践。我们定义精确度 医学作为发现机械锚定和临床相关疾病亚组， 可以遵循哪些最佳策略。精确医学统计方法， 开发和应用将改善对个人疾病状态的临床评估， 轨迹，以及相互竞争的干预措施可能带来的好处，从而使患有艾滋病的人受益。 风湿性疾病在过去的资助期间，由P30 PI安东尼罗森和 DSC总监Scott Zeger设计并实施了JH Precision Medicine Analytics 平台（PMAP），一个基于云的数据系统：（1）接收每晚下载的所有JHM临床 数据并将数据投影到安全的临床队列数据库中;（2）提供环境 协作，现代统计分析;（3）提供工具，从内部可视化 电子医疗记录与特定患者的决策相关的分析结果。 PMAP已经在硬皮病和肌炎中心实施。期间 未来一段时间，PMAP将活跃在每个卓越的流变学中心， 目前的临床队列数据库将构成下一阶段的信息基础， P30程序。这种新型的基础设施为DSC的开发和应用提供了可能 新的统计工具，确定疾病亚组，为他们的最佳治疗， 评估。通过这种方式，JHM流变学将发现关键的数据科学工具， 学习医疗保健系统。 数据科学核心的具体目标是：（1）提供研究设计和分析 支持调查人员生成、管理、分析和解释复杂数据 利用现代统计和计算方法;（2）开发和应用多元层次 纵向数据集，以识别预测疾病的患者子集 轨迹和主要临床事件;（3）应用现代统计分析方法， 严格评估治疗效果和安全性的观察性和实验性研究， 承认个体对治疗的反应可能存在异质性， 使用观察数据来解决因果问题所固有的潜在偏差。