UCSF Data Science Training to Advance Behavioral and Social Science Expertise for Health Research (DaTABASE) Program

加州大学旧金山分校数据科学培训，以促进健康研究的行为和社会科学专业知识 (DaTABASE) 计划

• 批准号: 10324595
• 负责人: William Brown
• 金额: $ 22.35万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-13 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10324595
• 关键词: Data Science; Databases; Health; Research; Training; behavioral/social science; programs

Summary Behavioral and social science researchers are essential for advancing research on reducing health disparities. The next generation of such researchers must be equipped with the most powerful, contemporary analytic tools. Newly available data and methods have the potential to transform the questions asked, the research designs used, and the statistical approaches applied to deepen our understanding of fundamental drivers of health and increase our capacity to improve population health and reduce disparities. To take advantage of these opportunities, we need researchers with interdisciplinary training that allows them to connect the traditional behavioral and social science expertise with the rapidly evolving technical repertoire of computational health scientists. The UCSF Data Science Training to Advance Behavioral and Social Science Expertise for Health Research (DaTABASE) program braids training strands previously operating independently within UCSF. Particular strengths of DaTABASE include: 1) Grounding in a rigorous, interdisciplinary quantitative training program for a foundation in study designs and statistical analyses to deliver actionable evidence on health; 2) Integration of multiple data streams, including: ‘omics, clinical (e.g., UC-wide, geolocated and longitudinal medical records, clickstream data from UCSF’s unique clinical informatics infrastructure), digital (device-based data flows, social media data, technology enabled cohorts), population (e.g., San Francisco Department of Public Health data linking health and social services provided to homeless individuals or other vulnerable populations) and policy data sets (e.g., local policies regulating cannabis retail outlets in California communities); 3) training and research activities organized around the elimination of health disparities. DaTABASE trainees will complete the rigorous methodological training for a PhD in Epidemiology and Translational Science. Additional content training, overseen by faculty in the UCSF Center for Health and Community, will provide theoretical frameworks and disciplinary background for social and behavioral sciences. Additional quantitative methods training will be delivered via the Bakar Computational Health Sciences Institute (BCHSI). BCHSI emphasizes machine learning tools to mine data sources for novel insights and discovery, including precision medicine and population health. This training initiative will prepare graduates to apply those analytic tools to novel data sets for research on behavioral and social processes underlying health disparities. The DaTABASE program will provide intensive mentoring from both content and methods experts. DaTABASE alumni will be prepared to lead research addressing current and emerging public health challenges with innovative computational and data science analytic approaches.

概括 行为和社会科学研究人员对于推进减少健康差异的研究至关重要。 下一代此类研究人员必须配备最强大的当代分析能力 新的数据和方法有可能改变所提出的问题和研究。 使用的设计和应用的统计方法来加深我们对基本驱动因素的理解 健康并提高我们改善人口健康和减少差距的能力。 这些机会，我们需要接受过跨学科培训的研究人员，使他们能够将 传统的行为和社会科学专业知识与快速发展的技术库 加州大学旧金山分校的计算健康科学家促进行为和社会科学的数据科学培训。 健康研究专业知识 (DaTABASE) 计划将之前运行的培训内容编成辫子 DaTABASE 的独特优势包括： 1) 以严格的、 跨学科定量培训计划，为研究设计和统计分析奠定基础 提供有关健康的可行证据；2) 整合多个数据流，包括：“组学、临床”（例如， UC 范围内的、地理定位的纵向医疗记录、来自 UCSF 独特临床的点击流数据 信息学基础设施）、数字化（基于设备的数据流、社交媒体数据、技术支持的群体）、 人口（例如，旧金山公共卫生部的数据将提供的健康和社会服务联系起来 无家可归的个人或其他弱势群体）和政策数据集（例如，规范 3) 围绕以下方面组织的培训和研究活动 消除健康差异 DaTABASE 学员将完成严格的方法培训 流行病学和转化科学博士。额外的内容培训，由加州大学旧金山分校的教师监督。 健康与社区中心，将为社会提供理论框架和学科背景 定量附加方法培训将通过 Bakar 计算提供。 健康科学研究所 (BCHSI) 强调机器学习工具来挖掘小说的数据源。 该培训计划将为洞察和发现做好准备，包括精准医学和人口健康。 毕业生将这些分析工具应用于新颖的数据集，以研究行为和社会过程 DaTABASE 计划将从内容和方面提供强化指导。 数据库校友将准备好领导针对当前和新兴公众的研究。 通过创新的计算和数据科学分析方法应对健康挑战。