Why we need a small data paradigm
Why we need a small data paradigm
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DOI：
10.1186/s12916-019-1366-x
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发表时间：
2019-07-17
期刊：
影响因子：
9.3
通讯作者：
Sim, Ida
中科院分区：
文献类型：
--
作者：
Hekler, Eric B.;Klasnja, Predrag;Sim, Ida
背景人们对个性化或精确医学的概念非常感兴趣和兴奋，特别是通过各种大数据的努力来推进这一愿景。虽然这些方法是必要的，但它们不足以实现完全个性化的医疗承诺。还需要一个严格的、互补的小数据范式，既可以独立于大数据，也可以与大数据协作。通过“小数据”，我们建立在埃斯特林的公式基础上，并指的是由特定的1个单位中的N个单位严格使用数据（即，个人、诊所、医院、医疗保健系统、社区、城市等）这篇文章的目的是阐明为什么需要一个小数据范式，并且它本身是有价值的，并为未来的工作提供初步的方向，这些工作可以推进研究设计和数据分析技术，以实现精确健康的小数据方法。从科学的角度来看，小数据方法的核心价值在于，与大数据相比，它可以独特地管理复杂的，动态的，多原因的，特殊表现的现象，如慢性病。除此之外，小数据方法更好地协调了科学和实践的目标，这可以用更少的数据实现更快的敏捷学习。还有一条可行的独特途径，可从小数据方法获得可转移的知识，这是对大数据方法的补充。未来的工作应（1）进一步完善小数据方法的适当方法;（2）推进将小数据方法更好地整合到现实世界实践中的战略;（3）推进积极整合小数据和大数据方法的优势和局限性的方法，并通过强大的因果关系科学将其连接到统一的科学知识库中。也就是说，小数据和大数据范式可以而且应该通过因果关系的基础科学结合起来。结合这些方法，可以实现精准健康的愿景。
BackgroundThere is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various big data' efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary small data' paradigm that can function both autonomously from and in collaboration with big data is also needed. By small data' we build on Estrin's formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit.Main bodyThe purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality.ConclusionSmall data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.