在人工胰腺中持续增加葡萄糖,胰岛素和生活方式数据,使用自适应生成和歧视模型
概括
这项研究介绍了一种新的生成对抗网络 (GAN),用于人工胰腺系统创建合成患者数据. 这种方法提高了数据的可用性,并提高了糖尿病管理中的葡萄糖预测准确度.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 人工胰腺系统依赖于各种数据来准确剂量胰岛素.
- 由于设备错误或患者不合规导致的数据缺口可能会阻碍闭环糖尿病管理.
- 高质量的合成数据生成对于增强现实世界数据集至关重要.
研究的目的:
- 开发和评估一种生成对抗网络 (GAN) 模型,用于人工胰腺系统中高质量的合成数据生成.
- 调查输出序列长度对生成和歧视模型选择的影响.
- 改进数据增强策略,以提高葡萄糖预测准确度.
主要方法:
- 一个基于GAN的新型架构被设计为根据所需的输出序列长度自动选择生成器和区分器模型.
- 该模型被训练来生成合成数据,包括单个患者的葡萄糖水平,体力活动和食信息.
- 生成的数据与真实数据相结合,以训练和评估葡萄糖预测模型.
主要成果:
- 拟议的GAN模型证明了有效生成合成葡萄糖,体力活动和食数据.
- 俄俄州T1DM和俄俄州T1D数据集的歧视性评分表明了高质量的合成数据生成.
- 使用真实和合成数据混合的葡萄糖预测模型比仅使用真实数据的模型获得更好的RMSE和MARD得分.
结论:
- 在GAN中基于序列长度的架构选择导致了对多个输出序列的优异合成数据生成.
- 开发的模型有效地增加了数据集,提高了人工胰腺应用中的葡萄糖预测的准确性.
- 这种方法为解决糖尿病管理系统数据短缺问题提供了一个有希望的解决方案.
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