用大数据和小数据理解衰老
Hiroko H Dodge1, Deborah Estrin2
1Kevreson Professor of Neurology at University of Michigan, and Professor of Neurology at Oregon Health & Science University.
概括
个人健康轨迹因累积的生活经历而有很大差异. 通过分析个体独特的时间数据 ("小数据"),而不是依赖群体平均值,可以获得个性化的健康见解.
科学领域:
- 老年学是一门学科.
- 个性化医疗是个性化的医疗.
- 生物医学数据科学 生物医学数据科学
背景情况:
- 个人健康和衰老是由整个生命中遗传和环境因素的独特组合所塑造的.
- 累积的生活经历导致老龄化率和对疾病的抵抗力显著的个体内变化.
- 标准化的群体数据 (例如,平均值,中位数) 往往不能充分预测个体的临床状态和预后.
研究的目的:
- 解决基于群体的健康数据在预测个人结果方面的局限性.
- 探索针对个性化的健康评估和管理的数据驱动的替代方法.
- 突出利用个人时间数据为量身定制的医疗保健的潜力.
主要方法:
- 对比"大数据" (大群体) 与"小数据" (个体时间流) 的实用性.
- 建议分析来自单个个人的丰富,纵向数据 ("小数据").
- 重点是随着时间的推移处理和解释组合的单个数据流.
主要成果:
- 高的个体内变异性需要个性化方法超越规范的群体数据.
- "小数据"方法,专注于个体时间模式,提供了一个可行的替代方案.
- 利用一个人的独特数据流可以提高诊断准确性和预后预测.
结论:
- 仅仅依靠群体数据是不够的准确的个人健康评估.
- 利用一个人的综合时间数据 ("小数据") 可实现定制的诊断和管理.
- 这种方法支持增强的患者护理,自我护理,持续的独立性和研究.
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