Disaggregating asthma: Big investigation versus big data.
Disaggregating asthma: Big investigation versus big data.
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DOI：
10.1016/j.jaci.2016.11.003
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发表时间：
2017-02
期刊：
影响因子：
--
通讯作者：
Custovic A
中科院分区：
文献类型：
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作者：
Belgrave D;Henderson J;Simpson A;Buchan I;Bishop C;Custovic A
我们正面临着一个重大的挑战，在确定哮喘亚型之间的差距差距，以了解因果机制，并将这些知识转化为个性化的预防和管理策略。近年来，“大数据”被视为产生假设和推动医疗保健新领域的灵丹妙药;数据必须而且将为自己说话的想法正在迅速成为一种新的教条。医疗保健数据和数据分析计算工具的现成可访问性的危险之一是，数据挖掘过程可能与临床解释、理解数据来源和外部验证的科学过程脱钩。虽然计算方法的进步对于使用数据中的意外结构来生成假设可能是有价值的，但仍然需要以科学的严谨性来测试假设和解释结果。我们认为，结合数据和假设驱动的方法在一个仔细的协同作用，仔细表征的出生和患者队列的重要性，在这个过程中的遗传，表型，生物和分子数据不能过分强调。未来的主要挑战是以产生有意义的临床解释的方式利用更大的医疗保健数据，并将其转化为更好的诊断和适当的个性化预防和治疗计划。迫切需要采用数据科学的综合方法进行跨学科研究，使基础科学家，临床医生，数据分析师和流行病学家共同努力了解哮喘的异质性。
We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand causal mechanisms and translating this knowledge into personalized prevention and management strategies. In recent years, “big data” has been sold as a panacea for generating hypotheses and driving new frontiers of health care; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of health care data and computational tools for data analysis is that the process of data mining can become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data, and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data- and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological, and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness bigger health care data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma.