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优化2型糖尿病管理:人工智能增强的持续葡萄糖监测数据的时间序列分析,用于个性化的饮食干预

Madiha Anjum¹, Raazia Saher¹, Muhammad Noman Saeed²

¹Department of Computer Engineering College of Computer Science and IT, King Faisal University, Alahsa, Saudi Arabia.

PeerJ. Computer science

|April 30, 2024

概括

这项研究开发了一个使用持续葡萄糖监测 (CGM) 和机器学习的AI系统,以帮助2型糖尿病患者通过个性化饮食调整,改善生活方式和血糖控制来管理血糖.

科学领域:

生物医学工程生物医学工程
医疗保健中的人工智能
内分泌学在内分泌学.

背景情况:

2型糖尿病 (T2DM) 的管理是具有挑战性的,通常由生活方式因素和血糖波动加剧.
有效改变患者的生活方式,特别是饮食,对于控制高血糖和低血糖至关重要.
持续葡萄糖监测 (CGM) 为了解血糖趋势提供了有价值的数据.

研究的目的:

开发一个人工智能辅助的数字系统,整合CGM数据,为T2DM患者提供个性化的生活方式和饮食建议.
通过优化基于预测的葡萄糖趋势和食物消费的饮食来减少血糖水平的变化.
授权T2DM患者管理他们的病情并达到目标HbA1c水平.

主要方法:

使用时间序列分析和预测模型 (XGBoost,SARIMA,Prophet) 来训练T2DM患者的CGM数据.
集成了一个人工智能驱动的食品识别解决方案,以跟踪营养摄入量及其对葡萄糖水平的影响.
开发了一个交互式系统,将预测的葡萄糖水平与饮食摄入量进行实时调整.

主要成果:

使用在CGM数据上训练的机器学习模型准确预测血糖水平.
通过时间序列分析识别不规则的葡萄糖模式和趋势.
证明了系统调整饮食以保持血糖在正常范围内的能力.

结论:

一个人工智能辅助的CGM系统可以有效地帮助T2DM患者通过个性化的饮食干预来管理血糖波动.
这种方法促进了更健康的生活方式,并支持实现目标HbA1c水平.
该系统为潜在的血糖事件提供及时警报,改善糖尿病自我管理.

关键词:

人工智能的人工智能是人工智能.糖尿病糖尿病糖尿病饮食计划饮食计划数字健康数字健康疾病管理疾病管理预测预测预测预测医疗信息学医学信息学时间序列时间序列可穿戴设备可以穿戴.养生保健养生保健是什么

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