人工智能和语义本体学用于个性化的活动 eCoaching 在健康的生活方式建议:一个元启发式方法
Ayan Chatterjee1,2, Nibedita Pahari3, Andreas Prinz4
1Department of Information and Communication Technology, Centre for E-Health, University of Agder, Grimstad, Norway. ayan.chatterjee@uia.no.
BMC medical informatics and decision making
|December 1, 2023
概括
本研究介绍了一种语义本体模型,用于自动化辅导 (eCoach) 中的个性化建议. 由人工智能驱动的方法通过整合活动预测和用户偏好以提供量身定制的健康指导来增强生活方式管理.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 自动化辅导 (eCoach) 系统利用人工智能 (AI) 进行持续的健康监测和个性化建议,以促进健康的生活方式.
- 在人工智能驱动的健康应用中,语义本体对于知识表示,数据集成和信息检索至关重要.
研究的目的:
- 提出一个语义本体模型,用于注释AI预测,预测结果和个人偏好,以生成个性化的建议.
- 开发一种混合方法,整合单变时间序列预测和组合多类分类,用于混合活动投影.
- 通过剩余误差最小化 (REM) 提高预测准确性,并通过基于天真的间隔预测改进推呈现.
主要方法:
- 开发了一个语义本体学,以整合人工智能驱动的活动预测和个人偏好.
- 采用混合方法,结合单变时间序列预测和集合多类分类,增强REM和Naïve基于间隔预测.
- 利用SPARQL查询协议用于语义解释和从集成数据生成个性化建议.
主要成果:
- 带有REM的自回归模型在公共和私人数据集上的活动预测方面超过了没有REM的模型.
- 梯度提升分类器在分类日常体育活动水平方面实现了高准确率 (高达99.80%).
- 拟议的本体学和SPARQL协议有效地产生了可理解的个性化活动建议.
结论:
- 拟议的方法有效地结合了人工智能驱动的活动预测,分类和个人偏好,使用语义规则进行个性化建议.
- 这种方法为增强自动化辅导系统和促进更健康的生活方式提供了宝贵的资产.
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