An Interdisciplinary Program for Advanced Training in Health Data Analytics

拟议的培训计划为卡罗莱纳州增加了博士后和博士后培训 健康信息学计划(CHIP)，专注于先进的研究和 生物医学和卫生信息学方面的培训。CHIP是一个真正的跨学科项目， 由1)医学院、2)吉林斯全球公共卫生学院、 3)护理学院，4)伊希尔曼药学院，5)牙医学院，6) 信息与图书馆学；7)文理学院计算机科学系 科学。通过与地区卫生保健的密切伙伴关系，该方案得到进一步加强 专门从事生物制药、医疗保健、IT和分析的组织和公司。 认识到与健康数据分析和使用相关的许多挑战，三个 芯片培训计划的基本方面是：1)健康数据分析方法，2) 构建功能强大的交互式分析系统，支持定制和支持决策 为各种各样的医疗保健操作提供支持，以及3)解决翻译挑战 实施和管理利用健康数据分析的信息系统 有效地。鉴于芯片是一个高度跨学科的项目，它将利用其多样化的 教师、研究中心、实验室等丰富的资源，开发了三大类型的 这些活动是拟议的博士前和博士后高级培训方案的一部分。这个 三项活动是：1)临床数据科学的研究方法和分析技术， 影像信息学、精确医学和人口健康大数据分析，2)授课 基于机器学习、生物统计学、大数据管理和可视化课程的学习 根植于现实医疗保健问题的分析和附加课程，以及3)大型- 规模、面向人群的系统开发、部署和结果评估。

The proposed training program adds predoctoral and postdoctoral training to the Carolina Health Informatics Program (CHIP), which concentrates on both advancing research and training in biomedical and health informatics. CHIP is a truly interdisciplinary program co- created and co-sponsored by 1) School of Medicine, 2) Gillings School of Global Public Health, 3) School of Nursing, 4) Eshelman School of Pharmacy, 5) School of Dentistry, 6) School of Information & Library Science, and 7) Computer Science Department of the College of Arts & Sciences. The program is further strengthened by close partnerships with area health care organizations and companies specializing in biopharmaceuticals, heath care, IT and analytics. Recognizing the many challenges associated with health data analytics and use, the three foundational aspects of the CHIP training programs are: 1) Methods for health data analytics, 2) Building powerful interactive analytics systems that allow customization and support decision making for a wide variety of health care operations, and 3) Addressing translational challenges in implementing and managing information systems that leverage health data analytics effectively. Given that CHIP is a highly interdisciplinary program, it will draw upon its diverse faculty, research centers, laboratories, and other rich resources to develop three major types of activities as part of the proposed advanced predoctoral and postdoctoral training program. The three activities are: 1) Research methods and analytics techniques for clinical data science, imaging informatics, precision medicine, and big data analytics for population health, 2) Didactic learning based on courses in machine learning, biostatistics, big data management, and visual analytics and additional courses that are rooted in realistic health care problems, and 3) Large- scale, population-oriented system development, deployment, and outcome evaluation.