Methods Core

方法核心

• 批准号: 10680536
• 负责人: PATRICK J HEAGERTY
• 金额: $ 27.08万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-21 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680536
• 关键词: Adopted; Algorithms; Area; Artificial Intelligence; Automobile Driving; Biomedical Research; Biometry; Characteristics; Clinical; Clinical Data; Clinical Research; Clinical Services; Clinical Trials; Code; Collaborations; Collection; Communities; Creativeness; Data; Data Collection; Data Coordinating Center; Data Science; Development; Diagnosis; Diagnostic; Disease; Electronic Health Record; Electronics; Fostering; Foundations; Funding; Goals; Health Services Research; Health Technology; Heterogeneity; Informatics; Information Retrieval; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Leadership; Learning; Machine Learning; Measurement; Medical Imaging; Methodology; Methods; Mission; Modernization; Monitor; Musculoskeletal; Musculoskeletal Diseases; National Institute of Arthritis, and Musculoskeletal, and Skin Diseases; Natural Language Processing; Observational Study; Organization administrative structures; Outcome; Patient Outcomes Assessments; Patients; Phase; Phenotype; Positioning Attribute; Prevention; Procedures; Process; Radiology Specialty; Randomized; Reporting; Reproducibility; Research; Research Design; Research Methodology; Role; Secure; System; Telephone; Text; United States National Institutes of Health; Universities; Use Effectiveness; Validation; Washington; clinical center; clinical development; clinical predictors; comparative effectiveness; convolutional neural network; data quality; data science infrastructure; design; electronic medical record system; electronic structure; flexibility; health care delivery; high dimensionality; image processing; innovation; mHealth; machine learning method; novel; observational cohort study; pragmatic study; pragmatic trial; quality assurance; randomized trial; research data dissemination; statistical and machine learning; statistics; treatment effect; trial design; unstructured data

The goal of the Methodologic Core is to support high-impact clinical research focusing on the diagnosis, treatment, and prevention of musculoskeletal diseases. Specifically, the Methodologic Core will provide the contemporary data science infrastructure necessary for studies that leverage electronic health records (EHR), and for trials that collect patient reported outcomes. We will organize around two major themes encompassing multiple activities: confirmatory learning which includes design and analysis of observational studies and randomized trials; and predictive learning which leverages contemporary machine learning and artificial intelligence for processing electronic health records data and other high-dimensional information. The data sciences are driving innovation in clinical research with the linkage of multiple EMR systems opening new opportunities for large-scale clinical learning. Ultimately rigorous research demands new methods for the extraction of reliable information and requires the design of trials that can tailor to the unique characteristics of patients and the context of health care delivery systems. The University of Washington Center for Biomedical Statistics (CBS) has provided informatics and statistical leadership to multiple observational and randomized studies focusing on musculoskeletal disorders and is uniquely positioned to provide the foundation for expanded impact within the broad scope of the NIAMS mission.

方法核心的目标是支持高影响力的临床研究，重点关注 肌肉骨骼疾病的诊断、治疗和预防。具体来说， Methodologic Core 将提供必要的当代数据科学基础设施 利用电子健康记录 (EHR) 的研究以及收集患者报告的试验 结果。我们将围绕两大主题组织多项活动： 验证性学习，包括观察性研究的设计和分析 随机试验；以及利用当代机器学习和预测学习 用于处理电子健康记录数据和其他高维数据的人工智能 信息。数据科学正在推动临床研究的创新 多个 EMR 系统为大规模临床学习带来了新的机会。最终 严格的研究需要新的方法来提取可靠的信息，并且需要 试验的设计可以根据患者的独特特征和背景进行调整 医疗保健提供系统。华盛顿大学生物医学统计中心 （哥伦比亚广播公司）为多个观察和统计提供了信息学和统计领导 专注于肌肉骨骼疾病的随机研究，具有独特的定位，可以提供 为在 NIAMS 使命的广泛范围内扩大影响奠定了基础。