餐后高血糖预警与机器学习解释

Asiful Arefeen, S. Fessler, Carol Johnston, H. Ghasemzadeh
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引用次数: 2

摘要

餐后高血糖(PPHG)对健康有害,会增加心血管疾病、视力下降和危及生命的疾病(如癌症)的风险。在PPHG事件发生之前进行检测可能有助于提供早期干预措施。先前的研究表明,PPHG事件可以根据饮食信息来预测。然而,这种计算方法(1)需要大量的数据来进行算法训练;(2)作为一个黑盒,缺乏可解释性,从而限制了这些技术在临床干预中的应用。基于这些缺点,我们提出了DietNudge1,这是一个基于机器学习的框架,它集成了关于饮食、胰岛素和血糖的多模态数据,可以在PPHG事件发生之前预测它们。使用糖尿病患者的数据,我们证明我们的模型可以预测PPHG事件,分类准确率高达90%,平均F1得分为0.93。建议的基于决策树的方法还可以识别导致即将发生的PPHG事件的可修改因素,同时提供个性化的阈值来防止此类事件的发生。我们的研究结果表明,我们可以开发简单而有效的计算算法,用于糖尿病和肥胖管理的预防机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forewarning Postprandial Hyperglycemia with Interpretations using Machine Learning
Postprandial hyperglycemia (PPHG) is detrimental to health and increases risk of cardiovascular diseases, reduced eyesight, and life-threatening conditions like cancer. Detecting PPHG events before they occur can potentially help with providing early interventions. Prior research suggests that PPHG events can be predicted based on information about diet. However, such computational approaches (1) are data hungry requiring significant amounts of data for algorithm training; and (2) work as a black-box and lack interpretability, thus limiting the adoption of these technologies for use in clinical interventions. Motivated by these shortcomings, we propose, DietNudge1, a machine learning based framework that integrates multi-modal data about diet, insulin, and blood glucose to predict PPHG events before they occur. Using data from patients with diabetes, we demonstrate that our model can predict PPHG events with up to 90% classification accuracy and an average F1 score of 0.93. The proposed decision-tree-based approach also identifies modifiable factors that contribute to an impending PPHG event while providing personalized thresholds to prevent such events. Our results suggest that we can develop simple, yet effective, computational algorithms that can be used as preventative mechanisms for diabetes and obesity management.
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