Big Data Coursework for Computational Medicine

描述：随着生物医学研究“大数据”时代的到来，包括表型、分子（包括组学）、临床、影像、行为和环境在内的多类型生物医学数据正在以前所未有的规模、高容量、高种类和高速度产生。这些数据集越来越庞大和复杂，挑战了我们目前的数据表示、集成和分析能力，以改善结果并降低医疗成本。众所周知，利用大数据的巨大潜力的最大挑战是教育和招聘未来的计算和数据科学家，他们具有掌握生物医学科学基础机会的背景、培训和经验。这需要跨学科的教育和实践培训，以理解大数据的应用、分析、限制和价值。为了弥补美国生物医学工作人员的知识差距，我们建议制定一个研究教育计划——计算医学大数据课程（BDC4CM）——通过提供量身定制的、深入的指导、动手实验模块和大数据访问、集成、处理和分析的案例研究，指导学生、研究员和科学家使用特定的大数据新方法和工具。该项目由来自梅奥诊所和明尼苏达大学的高跨学科和经验丰富的教师提供，将提供大数据方法和方法的短期培训机会，包括：1)数据和知识表示标准；2)信息提取和自然语言处理；3)可视化分析；4)数据挖掘与预测建模；5)隐私和道德；6)在比较有效性研究和人群健康研究与改善中的应用。我们的主要教育目标是为医疗保健大数据科学新兴的多维领域的下一代创新者和有远见的人做好准备，并培养满足行业需求的未来劳动力，提高美国在医疗保健技术和应用方面的竞争力。

DESCRIPTION: As the era of "Big Data" is dawning on biomedical research, multiple types of biomedical data, including phenotypic, molecular (including -omics), clinical, imaging, behavioral, and environmental data is being generated on an unprecedented scale with high volume, variety and velocity. These datasets are increasingly large and complex, challenging our current abilities for data representation, integration and analysis for improving outcomes and reducing healthcare costs. It is well-recognized that the greatest challenge to leveraging the significant potentials of Big Data is in educating and recruiting future computational and data scientists who have the background, training and experience to master fundamental opportunities in biomedical sciences. This demands interdisciplinary education and hands-on practicum training on understanding the application, analysis, limitations, and value of the Big Data. To bridge this knowledge gap for the U.S. biomedical workforce, we propose to develop a research educational program-Big Data Coursework for Computational Medicine (BDC4CM)-that will instruct students, fellows and scientists in the use of specific new methods and tools fo Big Data by providing tailored, in-depth instruction, hands-on laboratory modules, and case studies on Big Data access, integration, processing and analysis. Offered by highly interdisciplinary and experienced faculty from Mayo Clinic and the University of Minnesota, this program will provide a short- term training opportunity on Big Data methods and approaches for: 1) data and knowledge representation standards; 2) information extraction and natural language processing; 3) visualization analytics; 4) data mining and predictive modeling; 5) privacy and ethics; and 6) applications in comparative effectiveness research and population health research and improvement. Our primary educational goal is to prepare the next generation of innovators and visionaries in the emerging, multidimensional field of Big Data Science in healthcare, as well as to develop a future workforce that fulfills industry needs and increases U.S. competitiveness in healthcare technologies and applications.