凯姆-IoMT:知识图嵌入增强精确的医疗服务推,预防糖尿病
Nasrullah Khan1, Muhammad Rafiq Mufti2, Muhammad Arif1
1College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
这项研究引入了一个新的知识图 嵌入增强精确的医疗服务推 (KEM) 模型用于糖尿病护理. 通过整合多种数据源和用户评论,KEM改善了个性化的医疗服务建议.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 卫生推系统 卫生推系统
背景情况:
- 医疗物联网 (IoMT) 系统增强了个性化的医疗服务.
- 推系统 (RS) 面临着异质糖尿病数据的挑战,限制了准确的推.
- 现有的RS缺乏使用用户评论和疾病更新扩展知识库.
研究的目的:
- 为IoMT引入知识图嵌入增强精确的医疗服务推 (KEM) 模型.
- 为了提高RS的精度和上下文敏感性,用于糖尿病护理.
- 通过结合各种数据和用户反来解决当前RS的局限性.
主要方法:
- 数据收集:用户评论和在线疾病数据.
- 预处理和知识图 (KG) 转换.
- 使用图形神经网络 (GNN) 和深度矩阵分解 (DMF) 的KG嵌入.
主要成果:
- 该KEM模型有效地整合了异构的医疗数据,用户评论和在线疾病信息.
- 嵌入KG和DMF成功计算了用于准确推关系的潜在因子.
- 实验结果显示,KEM在性能方面明显优于基线方法.
结论:
- 在IoMT中,KEM模型在增强糖尿病护理的RS方面表现出卓越的有效性.
- 整合知识图和深度学习可以提高医疗服务建议的准确性和相关性.
- 这种方法为个性化医疗保健解决方案提供了一个有希望的方向.
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