Towards The Development of Subject-Independent Inverse Metabolic Models
Towards The Development of Subject-Independent Inverse Metabolic Models
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DOI：
10.1109/icassp39728.2021.9413829
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Seyed Sajjadi;Anurag Das;R. Gutierrez-Osuna;Theodora Chaspari;Projna Paromita;L. Ruebush;N. Deutz;B. Mortazavi
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作者：
Seyed Sajjadi;Anurag Das;R. Gutierrez-Osuna;Theodora Chaspari;Projna Paromita;L. Ruebush;N. Deutz;B. Mortazavi
饮食监测是2型糖尿病干预的一个重要组成部分，但这是一项耗时且往往不准确的工作。为了解决这个问题，我们描述了一种通过分析一餐后血糖的波动来自动监测饮食的方法。特别是，我们评估了三种标准化技术(基线校正、特征标准化和模型个性化)，它们可以用来补偿食物代谢中存在的巨大个体差异。然后，我们建立机器学习模型，根据相关的葡萄糖反应预测一顿饭中的常量营养素的数量。我们在一个包含15名参与者的血糖反应的数据集上对该方法进行了评估，这些参与者吃了9顿饭。有三种技术提高了模型的准确性：减去基线血糖，执行z-Score归一化，以及根据每个人的体重指数来衡量大量营养素的数量。
Diet monitoring is an important component of interventions in type 2 diabetes, but is time intensive and often inaccurate. To address this issue, we describe an approach to monitor diet automatically, by analyzing fluctuations in glucose after a meal is consumed. In particular, we evaluate three standardization techniques (baseline correction, feature normalization, and model personalization) that can be used to compensate for the large individual differences that exist in food metabolism. Then, we build machine learning models to predict the amounts of macronutrients in a meal from the associated glucose responses. We evaluate the approach on a dataset containing glucose responses for 15 participants who consumed 9 meals. Three techniques improve the accuracy of the models: subtracting the baseline glucose, performing z-score normalization, and scaling the amount of macronutrients by each individuals’ body mass index.