Stackable trainings in the FAIRification and AI/ML readiness of data with applications to environmental health and justice

数据公平化和人工智能/机器学习就绪性的可堆叠培训及其在环境健康和正义中的应用

• 批准号: 10405960
• 负责人: JULIA Green BRODY
• 金额: $ 8.62万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10405960
• 关键词: Data; Environmental Health; Readiness; Training; environmental justice

ABSTRACT The ability to find, combine, and analyze multiple large-scale biomedical datasets to make better and ethical decisions for the future of patients, populations, and health systems is now a set of necessary skills for modern analysts. However, most current data analytics and workshops focus on deriving or applying modern techniques, such as statistical learning procedures, PyTorch, TensorFlow, neural networks, and other large-scale prediction models, as opposed to the necessary steps involved in preparing data for such analyses. Further, the next (and current) generation of biomedical researchers must be cognizant of FAIR principles to be prepared to make their data accessible by machines in order to fully leverage the continued growth around methodological developments to properly analyze large amounts of data across multiple studies/systems/countries. In addition to a methodologic toolkit, educating the biomedical analyst workforce must include training to build their ability to locate and store data for future analyses in an automated manner. We propose a suite of stackable modules to provide a rich foundation to the existing robust educational offerings around the applications of AI/ML to biomedical data that many trainees already receive. Through our close partnerships with the NIEHS PROTECT Center and the multinational OHDSI community for observational health data science and informatics, our goal is to provide training to prepare data for AI and ML applications in a rigorous and reproducible way, understand the ethical issues around AI and ML, as well as receive hands-on training around FAIR principles for storing and accessing such data. These modules will prepare researchers for successful careers as data analysts, ready to exploit the power of available AI/ML frameworks.

抽象的 能够查找、组合和分析多个大规模生物医学数据集，以做出更好且合乎道德的数据 为患者、人口和卫生系统的未来做出的决策现在已成为现代社会的一套必要技能。 分析师。然而，当前大多数数据分析和研讨会都侧重于推导或应用现代数据 技术，例如统计学习程序、PyTorch、TensorFlow、神经网络等 大规模预测模型，而不是为此类分析准备数据所涉及的必要步骤。 此外，下一代（和当前）生物医学研究人员必须认识到公平原则 准备好让机器访问他们的数据，以充分利用周围的持续增长 正确分析多个领域的大量数据的方法论发展 研究/系统/国家。除了方法工具包之外，还对生物医学分析师队伍进行教育 必须包括培训，以培养他们以自动化方式定位和存储数据以供将来分析的能力。 我们提出了一套可堆叠模块，为现有强大的教育提供丰富的基础 许多学员已经收到了围绕人工智能/机器学习在生物医学数据中的应用的产品。通过我们的 与 NIEHS PROTECT 中心和跨国 OHDSI 社区密切合作， 观察健康数据科学和信息学，我们的目标是提供培训，为人工智能和机器学习准备数据 以严格且可重复的方式进行应用程序，了解有关人工智能和机器学习的道德问题，以及 接受有关存储和访问此类数据的 FAIR 原则的实践培训。这些模块将 帮助研究人员为数据分析师的成功职业生涯做好准备，准备充分利用现有人工智能/机器学习的力量 框架。