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Dietary assessment can be based on pattern recognition rather than recall

机译：饮食评估可以基于模式识别而不是召回

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Diet is the leading predictor of health status, including all-cause mortality, in the modern world, yet is rarely measured; whereas virtually every adult in a developed country knows their approximate blood pressure, hardly any knows their objective diet quality. Leading authorities have called for the inclusion of nutrition in every electronic health record as one of the many remedial steps required to give dietary quality the routine attention it warrants. Existing tools to capture dietary intake are based on either real-time journaling or recall. Journaling, or logging, is time and labor intensive. Recall is notoriously unreliable, as humans are notably bad at remembering detail. Even allowing for the challenge of recall, these dietary intake methods are labor and time intensive, and require analysis at the n-of-1 level. We hypothesize that dietary intake assessment can be "reverse engineered"-predicating assessment on the recognition of fully formed dietary patterns-rather than endeavoring to assemble such a representation one food, meal, dish, or day at a time. This pattern recognition-based method offers potential advantages over existing methods, including speed, efficiency, cost, and applicability. We have developed and provisionally tested such a system, and the results thus far support our hypothesis. We are convinced that leveraging pattern recognition to make dietary assessment quick, user-friendly, economical, and scalable can allow for the conversion of dietary quality into a universally measured and routinely managed vital sign. In this paper, we present the supporting case.
机译：饮食是健康状况的领先预测因子，包括在现代世界中的全因死亡率，但很少被衡量;然而，几乎一个发达国家的每个成年人都知道他们的近似血压，几乎没有任何了解他们的客观饮食质量。领先的当局要求将每一种电子健康记录中的营养纳入作为提供饮食质量所需的许多补救措施之一，即其认股权证的常规关注。捕获膳食摄入的现有工具基于实时日记或召回。日记或伐木，是时间和劳动密集型。召回是众所周知的不可靠，因为人类在记住细节时非常糟糕。甚至允许召回的挑战，这些膳食进气方法是劳动力和时间密集，需要在N-1水平上进行分析。我们假设饮食摄入量评估可以是“反向工程” - 关于识别完全形成的饮食模式的评估 - 而不是努力一次组装一种食物，膳食，菜肴或一天。基于模式识别的方法提供了与现有方法相比的潜在优势，包括速度，效率，成本和适用性。我们已经开发并临时测试了这样的系统，因此迄今为止的结果支持我们的假设。我们相信，利用模式识别来使饮食评估快速，用户友好，经济，可扩展可以允许将饮食质量转化为普遍测量和常规管理的生命体征。在本文中，我们介绍了支持案例。

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