Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Per Med. 2019 May 1;16(3):247-257. doi: 10.2217/pme-2018-0145. Epub 2019 Feb 14.
Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data are being stored in electronic health records yielding big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making. We end with some specific training recommendations for preparing next-generation scientists for biomedical big data.
个性化医疗正在通过我们测量患者的生物和环境信息的能力得以实现。这些数据中的大部分都存储在电子健康记录中，形成了大数据，这给其管理和分析带来了挑战。在这里，我们回顾了下一代科学家充分挖掘生物医学大数据潜力所需的几个知识领域。我们首先概述了大数据及其存储和管理。然后，我们回顾了统计学和数据科学这两个基础主题，接着是人工智能、机器学习和自然语言处理的核心课程，这些都是为临床决策制定预测模型所必需的。最后，我们为准备下一代生物医学大数据科学家提出了一些具体的培训建议。
