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

摘要 能够查找、联合收割机和分析多个大规模生物医学数据集， 为病人、人口和卫生系统的未来做出决定，现在是现代医疗保健的一套必要技能。 分析机构的然而，大多数当前的数据分析和研讨会侧重于导出或应用现代数据分析方法。 技术，如统计学习程序，PyTorch，TensorFlow，神经网络等 大规模预测模型，而不是为这种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， 准备让机器访问他们的数据，以充分利用周围的持续增长， 方法的发展，以正确地分析大量的数据，在多个 研究/系统/国家。除了方法学工具包，教育生物医学分析人员 必须包括培训，以培养他们以自动方式查找和存储数据以供未来分析的能力。 我们提出了一套可堆叠的模块，为现有的强大的教育 围绕AI/ML应用于生物医学数据的产品，许多学员已经收到。通过我们 与NIEHS CRTIT中心和跨国OHDSI社区建立了密切的合作关系， 观察健康数据科学和信息学，我们的目标是提供培训，为AI和ML准备数据 以严格和可复制的方式应用，了解围绕AI和ML的伦理问题，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 为研究人员作为数据分析师的成功职业做好准备，准备利用可用的AI/ML的力量 框架。

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.