Big Data Coursework for Computational Medicine

计算医学大数据课程

• 批准号: 9242970
• 负责人: CHRISTOPHER G CHUTE
• 金额: $ 6.83万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9242970
• 关键词: Academic Medical Centers; Address; Advisory Committees; Area; Behavioral; Big Data; Bioethics; Biological Sciences; Biomedical Research; Case Study; Clinic; Clinical; Collection; Committee Members; Complex; Computational Biology; Data; Data Reporting; Data Science; Data Set; Development; Development Plans; Discipline; Doctor of Medicine; Doctor of Philosophy; Education; Effectiveness; Engineering; Ethics; Evaluation; Faculty; Feedback; Future; Goals; Grant; Health; Health Care Costs; Health Services Research; Healthcare; Image; Imagery; Industry; Informatics; Instruction; Interdisciplinary Education; Interview; Knowledge; Laws; Learning; Mathematics; Measures; Medicine; Mentorship; Methods; Minnesota; Molecular; Monitor; Natural Language Processing; Patient-Focused Outcomes; Peer Review; Performance; Positioning Attribute; Postdoctoral Fellow; Privacy; Process; Program Reviews; Public Health; Publications; Recruitment Activity; Research; Research Personnel; Research Project Grants; Resources; Science; Scientist; Students; Surveys; Technology; Training; Training Activity; Training Programs; United States National Institutes of Health; Universities; base; big biomedical data; biomedical informatics; career; career development; collaborative environment; comparative effectiveness; computer science; data access; data mining; education research; effectiveness research; experience; faculty mentor; improved; improved outcome; information organization; instrument; laboratory experience; laboratory module; meetings; multidisciplinary; new technology; next generation; population health; predictive modeling; programs; skills; statistics; student training; success; tool; training opportunity; working group

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.

产品说明：随着“大数据”时代在生物医学研究中的到来，多种类型的生物医学数据，包括表型、分子（包括组学）、临床、成像、行为和环境数据，正在以前所未有的规模以高容量、多样性和速度产生。这些数据集越来越大，越来越复杂，挑战着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本机会的背景，培训和经验。这需要跨学科教育和实践培训，以了解大数据的应用，分析，局限性和价值。为了弥合美国生物医学劳动力的这一知识差距，我们建议开发一个研究教育计划-计算医学大数据课程（BDC 4CM）-这将指导学生，研究员和科学家使用特定的新方法和工具，通过提供量身定制的，深入的指导，动手实验室模块，以及大数据访问，集成，处理和分析的案例研究。由来自马约诊所和明尼苏达大学的高度跨学科和经验丰富的教师提供，该计划将提供关于大数据方法和方法的短期培训机会：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）数据挖掘和预测建模; 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在医疗保健大数据科学的新兴多维领域培养下一代创新者和远见者，以及培养满足行业需求并提高美国在医疗保健技术和应用方面竞争力的未来劳动力。