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实现和数字化以患者为中心的日常功能:功能组学的一个案例。

Operationalizing and digitizing person-centered daily functioning: a case for functionomics.

机构信息

Radboud Institute for Health Sciences, IQ health, Radboud university medical centre, Nijmegen, The Netherlands.

School of Allied Health, HAN University of Applied Sciences, Nijmegen, The Netherlands.

出版信息

BMC Med Inform Decis Mak. 2024 Jun 27;24(1):184. doi: 10.1186/s12911-024-02584-2.

Abstract

An ever-increasing amount of data on a person's daily functioning is being collected, which holds information to revolutionize person-centered healthcare. However, the full potential of data on daily functioning cannot yet be exploited as it is mostly stored in an unstructured and inaccessible manner. The integration of these data, and thereby expedited knowledge discovery, is possible by the introduction of functionomics as a complementary 'omics' initiative, embracing the advances in data science. Functionomics is the study of high-throughput data on a person's daily functioning, that can be operationalized with the International Classification of Functioning, Disability and Health (ICF).A prerequisite for making functionomics operational are the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This paper illustrates a step by step application of the FAIR principles for making functionomics data machine readable and accessible, under strictly certified conditions, in a practical example. Establishing more FAIR functionomics data repositories, analyzed using a federated data infrastructure, enables new knowledge generation to improve health and person-centered healthcare. Together, as one allied health and healthcare research community, we need to consider to take up the here proposed methods.

摘要

越来越多的关于个人日常功能的数据正在被收集,这些数据包含了能够彻底改变以患者为中心的医疗保健的信息。然而,由于这些数据主要以非结构化和不可访问的方式存储,其全部潜力尚未得到充分利用。通过引入功能组学作为一种补充的“组学”计划,并结合数据科学的进步,这些数据可以实现整合,从而加速知识发现。功能组学是对个人日常功能的高通量数据进行研究,可以通过国际功能、残疾和健康分类(ICF)进行操作。使功能组学具有操作性的前提条件是 FAIR(可发现、可访问、可互操作和可重复使用)原则。本文通过一个实际案例,说明了在严格认证的条件下,逐步应用 FAIR 原则,使功能组学数据具有机器可读性和可访问性。建立更多符合 FAIR 标准的功能组学数据存储库,并使用联合数据基础设施进行分析,可以生成新的知识,从而改善健康和以患者为中心的医疗保健。作为一个联合的医疗保健和医疗研究社区,我们需要考虑采用这里提出的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ece7/11212415/a170c4238ad3/12911_2024_2584_Fig1_HTML.jpg

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