首页> 外文期刊>Frontiers in Medicine >Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network
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Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network

机译:在肿瘤学中的真实证据:需要生物医学大数据洞察提供联邦网络

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Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V's of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V's whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.
机译:向新的自适应途径迈出开发和进入创新药物意味着在整个药品生命周期中收集的现实数据(RWD)变得越来越重要。 RWD的大数据分析可以获得对药物的有效性的新的和强大的见解。然而,医疗保健生态系统仍然面临许多特定的挑战,妨碍使用大数据分析提供现实世界证据(RWE)。我们区分探索性(EXTE)和假设评估(HETE)研究在现实世界中测试治疗效果。从我们的经验和在数据管理的四个V的背景下,我们表明,为了获得有意义的结果,无论进行的研究类型如何,都需要多种和准确性。因此,对于EXTE研究,需要高数据量,而HETE研究高速变得至关重要。接下来,我们突出了生物医学大数据生态系统中所需要的内容,存在:(a)国际数据可重用性; (b)实时RWD处理信息系统; (c)纵向rwd。最后,在努力管理四个v的同时,尊重患者隐私法律,我们争辩于在共同数据模型上开发一个底层联邦的RWD基础设施,能够将集中进行的大数据分析带到去集中保存的生物医学数据。

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