哮喘大数据：越大且越美？
2018/01/05
摘要
“大数据”十分流行，其主要被用于反应未来科学技术发展所面临的重大挑战以及最可能的解决方法，包括航空、气候变化，经济，健康和疾病。与其他流行趋势相似，大数据代表对于不同的人群发生不同的情况。在医学上，大数据用于描述大样本人群的流行病学研究，高亲和性，多维度数据，且往往相互交错。大数据通常捕捉单一时间点的信息，但无法提供慢性疾病短时间的信息，包括每天变异性；对于干扰的反应，例如间歇性感染；疾病的好转或对于治疗的反应，通常无法收集整个生命周期的信息。因此，观察经常受限于所需要测量的内容，时间和对象，且只能提供样本采集组中真实情况的估算值。纳入大样本人群的大数据使临床信息更充分和具有普遍性。事实上，当研究人群或样本量达到很大或至少可观的时候，临床观察则不再是估算值，而是一个人群情况的基本描述。
Big asthma data: getting bigger and more beautiful?
Thorax Published 29 December 2017, doi:10.1136/thoraxjnl-2017-211148
Sarah Diver, Chris E Brightling
Abstract
‘Big data’ is on trend and the term is used in equal measure to reflect both one of the greatest challenges and likeliest solutions to future scientific advances from fundamental understanding in astrophysics, climate change, economics, health and disease. Like many trends, it means different things to different people. In medicine, it is used to describe the data derived from large populations in epidemiology studies, high fidelity multiscale ‘omic datasets across spatial scales within individuals or sometimes a combination of the two. Big data will often capture information at a single time point. Typically, it does not address temporal scales of chronic disease including day-to-day variability, response to perturbations such as intercurrent infection, decompensation of the disease or response to therapeutic interventions and is rarely obtained over a life course. Observations will therefore always be limited by what is measured, when and in whom and will only ever provide estimates of what is ‘real’ within the larger group from which the sample is taken. Big data that includes large populations makes interpretations more robust and generalisable. Indeed, as the population studied or sample size approaches a majority, or at least a sizeable minority, of the whole population then the observations begin to no longer be estimates, but simply a description of the population
“大数据”十分流行，其主要被用于反应未来科学技术发展所面临的重大挑战以及最可能的解决方法，包括航空、气候变化，经济，健康和疾病。与其他流行趋势相似，大数据代表对于不同的人群发生不同的情况。在医学上，大数据用于描述大样本人群的流行病学研究，高亲和性，多维度数据，且往往相互交错。大数据通常捕捉单一时间点的信息，但无法提供慢性疾病短时间的信息，包括每天变异性；对于干扰的反应，例如间歇性感染；疾病的好转或对于治疗的反应，通常无法收集整个生命周期的信息。因此，观察经常受限于所需要测量的内容，时间和对象，且只能提供样本采集组中真实情况的估算值。纳入大样本人群的大数据使临床信息更充分和具有普遍性。事实上，当研究人群或样本量达到很大或至少可观的时候，临床观察则不再是估算值，而是一个人群情况的基本描述。
（上海交通大学医学院附属瑞金医院呼吸与危重症医学科 周剑平 万欢英 摘译）
（Thorax Published 29 December 2017, doi:10.1136/thoraxjnl-2017-211148 ）
（Thorax Published 29 December 2017, doi:10.1136/thoraxjnl-2017-211148 ）
Big asthma data: getting bigger and more beautiful?
Thorax Published 29 December 2017, doi:10.1136/thoraxjnl-2017-211148
Sarah Diver, Chris E Brightling
Abstract
‘Big data’ is on trend and the term is used in equal measure to reflect both one of the greatest challenges and likeliest solutions to future scientific advances from fundamental understanding in astrophysics, climate change, economics, health and disease. Like many trends, it means different things to different people. In medicine, it is used to describe the data derived from large populations in epidemiology studies, high fidelity multiscale ‘omic datasets across spatial scales within individuals or sometimes a combination of the two. Big data will often capture information at a single time point. Typically, it does not address temporal scales of chronic disease including day-to-day variability, response to perturbations such as intercurrent infection, decompensation of the disease or response to therapeutic interventions and is rarely obtained over a life course. Observations will therefore always be limited by what is measured, when and in whom and will only ever provide estimates of what is ‘real’ within the larger group from which the sample is taken. Big data that includes large populations makes interpretations more robust and generalisable. Indeed, as the population studied or sample size approaches a majority, or at least a sizeable minority, of the whole population then the observations begin to no longer be estimates, but simply a description of the population
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