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Updated: Jun 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

对于血糖水平预测的深度学习:模型在不同的数据集中概括得有多好?

Sarala Ghimire¹, Turgay Celik², Martin Gerdes¹

¹Department of Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway.

|September 25, 2024

概括

长短期记忆网络 (LSTM) 模型在预测糖尿病患者的血糖水平方面表现出色,显示出卓越的准确性和概括性. 自我注意网络 (SAN) 也显示出糖尿病管理的强大预测能力.

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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
数据科学用于糖尿病管理管理

背景情况:

准确的血糖水平预测对于糖尿病患者护理和闭环治疗系统至关重要.
现有的葡萄糖预测深度学习模型可能会由于不同的方法和数据集而表现出偏差.
需要进行全面的比较来评估模型的适用性和通用性.

研究的目的:

为了比较各种深度学习模型的性能和通用性,用于预测血糖水平.
为了确定糖尿病管理中不同目标的最佳模型.
根据准确性和概括能力,提供对模型选择的见解.

主要方法:

评估了前神经网络 (FFN),卷积神经网络 (CNN),长期短期记忆网络 (LSTM),时间卷积神经网络 (TCNN) 和自我注意网络 (SAN).
利用四个不同的大小,年龄组和条件的数据集来测试概括.
采用根平均平方误差 (RMSE),平均绝对差异 (MAD),确定系数 (CoD),克拉克误差格 (CEG) 和科尔摩戈罗夫-斯米尔诺夫 (KS) 测试进行分析.

主要成果:

LSTM以最低的RMSE和最高的概括能力展示了最佳性能.
在预测性能和通用化方面,SAN紧密跟随LSTM.
尽管总体预测准确性较低,但FFN显示出捕捉趋势的潜力.

结论:

由于它们能够捕捉长期依赖,LSTM和SAN模型对血糖预测非常有效.
模型选择应与具体目标保持一致,平衡准确性和概括性要求.
这种比较分析有助于选择适合糖尿病护理和研究的深度学习模型.