论文标题
关于饮食行为和破折号饮食计划优化限制的开源数据集
An Open-Source Dataset on Dietary Behaviors and DASH Eating Plan Optimization Constraints
论文作者
论文摘要
线性约束优化技术已应用于许多实际设置。近年来，推断优化模型中未知参数和功能也已获得吸引力。该推论通常基于现有的观察和/或已知参数。因此，这样的模型需要可靠，易于访问并易于解释的示例才能评估。为了促进此类方向的研究，我们根据不同人群的饮食行为，其人口统计以及预先存在的条件以及其他因素提供了一个修改的数据集。该数据是从国家健康和营养考试调查（NHANES）中收集的，并补充了美国农业部（USDA）的营养数据。我们还为高血压和糖尿病前患者提供了量身定制的数据集，作为感兴趣的群体，他们可能会从诸如饮食中的饮食饮食方法中受益，例如停止高血压（DASH）饮食计划。将数据编译和策划，以使其适合于线性优化模型的输入。我们希望这些数据及其补充，开放式材料可以加速并简化对线性优化和约束推理模型的解释和研究。完整的数据集可以在以下存储库中找到：https：//github.com/cssehealthcare/inverselearning
Linear constrained optimization techniques have been applied to many real-world settings. In recent years, inferring the unknown parameters and functions inside an optimization model has also gained traction. This inference is often based on existing observations and/or known parameters. Consequently, such models require reliable, easily accessed, and easily interpreted examples to be evaluated. To facilitate research in such directions, we provide a modified dataset based on dietary behaviors of different groups of people, their demographics, and pre-existing conditions, among other factors. This data is gathered from the National Health and Nutrition Examination Survey (NHANES) and complemented with the nutritional data from the United States Department of Agriculture (USDA). We additionally provide tailored datasets for hypertension and pre-diabetic patients as groups of interest who may benefit from targetted diets such as the Dietary Approaches to Stop Hypertension (DASH) eating plan. The data is compiled and curated in such a way that it is suitable as input to linear optimization models. We hope that this data and its supplementary, open-accessed materials can accelerate and simplify interpretations and research on linear optimization and constrained inference models. The complete dataset can be found in the following repository: https://github.com/CSSEHealthcare/InverseLearning
