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

摘要 能够找到、组合和分析多个大规模生物医学数据集，从而使 为患者、人口和卫生系统的未来做出决定现在是现代社会的一套必要技能 分析家。然而，大多数当前的数据分析和研讨会都侧重于派生或应用现代 技术，如统计学习过程、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(也是现在的)生物医学研究人员必须认识到公平的原则 准备好让机器访问他们的数据，以便充分利用 正确分析多个数据库中的大量数据的方法发展 研究/系统/国家。除了方法学工具包外，还可以培训生物医学分析人员 必须包括培训，以建立他们以自动化方式定位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，为现有的稳健教育提供了丰富的基础 围绕AI/ML在生物医学数据中的应用提供的服务，许多学员已经收到了这些服务。通过我们的 与NIEHS保护中心和跨国OHDSI社区建立密切合作伙伴关系 观察卫生数据科学和信息学，我们的目标是提供培训，为人工智能和ML准备数据 以严格和可重复的方式应用程序，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公平原则的实践培训。这些模块将 让研究人员为作为数据分析师的成功职业生涯做好准备，准备好利用可用的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.