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大数据分析在法国体外诊断市场的潜力。

Potential of big data analytics in the French in vitro diagnostics market.

作者信息

Dubois Nicolas, Garnier Nicolas, Meune Christophe

机构信息

BIORANCE, Laboratoires Réunis ; Clinique de la Côte d'Emeraude, Saint-Malo, France.

ProbaYes SAS, Montbonnot, France.

出版信息

Ann Biol Clin (Paris). 2017 Dec 1;75(6):683-685. doi: 10.1684/abc.2017.1298.

Abstract

The new paradigm of the big data raises many expectations, particularly in the field of health. Curiously, even though medical biology laboratories generate a great amount of data, the opportunities offered by this new field are poorly documented. For better understanding the clinical context of chronical disease follow-up, for leveraging preventive and/or personalized medicine, the contribution of big data analytics seems very promising. It is within this framework that we have explored to use data of a Breton group of laboratories of medical biology to analyze the possible contributions of their exploitation in the improvement of the clinical practices and to anticipate the evolution of pathologies for the benefit of patients. We report here three practical applications derived from routine laboratory data from a period of 5 years (February 2010-August 2015): follow-up of patients treated with AVK according to the recommendations of the High authority of health (HAS), use of the new troponin markers HS and NT-proBNP in cardiology. While the risks and difficulties of using algorithms in the health domain should not be underestimated - quality, accessibility, and protection of personal data in particular - these first results show that use of tools and technologies of the big data repository could provide decisive support for the concept of "evidence based medicine".

摘要

大数据的新范式引发了诸多期待,尤其是在健康领域。奇怪的是,尽管医学生物学实验室产生了大量数据,但这个新领域所带来的机遇却鲜有文献记载。为了更好地理解慢性病随访的临床背景,为了利用预防和/或个性化医疗,大数据分析的贡献似乎非常有前景。正是在这个框架内,我们探索利用布列塔尼一组医学生物学实验室的数据,来分析其利用在改善临床实践方面的可能贡献,并为了患者的利益预测病理的演变。我们在此报告从5年期间(2010年2月 - 2015年8月)的常规实验室数据中得出的三个实际应用:根据法国卫生高级管理局(HAS)的建议对接受维生素K拮抗剂治疗的患者进行随访,在心脏病学中使用新型肌钙蛋白标志物高敏肌钙蛋白(HS)和N末端脑钠肽前体(NT-proBNP)。虽然在健康领域使用算法的风险和困难不可低估——尤其是个人数据的质量、可获取性和保护——但这些初步结果表明,使用大数据存储库的工具和技术可为“循证医学”概念提供决定性支持。

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