使用新媒体技术支持的XGBoost算法对老年人的身体和精神健康管理:使用CLHLS数据库中的医疗保健数据开发个性化的健康干预计划
Yutong Wang1, Xin Guan2, Shiyuan Qu3
1Researching Center of Social Security, Wuhan University, Wuhan, China.
Frontiers in public health
|June 16, 2025
概括
这项研究开发了一个人工智能为老年人提供数字健康平台,集成多模式数据,以准确评估健康风险和改善福祉. 该系统有效支持慢性疾病预防,提高生活质量.
科学领域:
- 老年学是指老年学的学科.
- 数字健康数字健康
- 人工智能的人工智能
背景情况:
- 人口老龄化需要先进的,个性化的健康管理解决方案.
- 现有的系统往往缺乏整合多源数据和智能风险评估能力.
- 新媒体技术为增强老年护理和福祉支持提供了潜力.
研究的目的:
- 为老年人提出一个全面的数字健康管理平台.
- 整合人工智能和区块链,以提供个性化和安全的医疗服务.
- 开发一个智能系统来评估老年人的健康风险,并支持预防慢性疾病.
主要方法:
- 开发了一个个性化的健康管理系统,使用多源数据,人工智能和区块链.
- 用户隐藏的迪里克莱特分配 (LDA) 来从健康数据中提取主题关键词.
- 将文本特征与ResNet50图像特征结合起来,用于多模式表示.
- 使用CLHLS数据构建了一个XGBoost模型用于健康风险评估.
主要成果:
- 在LDA+ResNet50模型中,健康主题分类的F1平均得分为0.926.
- "心理学"领域显示了最高的分类表现,F1得分为0.97.
- 在健康结果分类中,XGBoost模型实现了0.95的准确性,识别了复杂的模式.
结论:
- 证明了将主题建模,深度学习和机器学习用于老年人健康风险评估的可行性.
- 拟议的方案提高了数字健康管理的准确性和智能性.
- 该平台有助于预防慢性疾病,并改善老年人的生活质量.
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