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针对病毒性传染病的新型基于图形的机器学习技术:对流感和肝炎疾病的应用
Eman Alqaissi1,2, Fahd Alotaibi1, Muhammad Sher Ramzan1
1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Annals of medicine
|January 19, 2024
概括
这项研究引入了一种新的医学知识图 (MKG) 和机器学习 (ML) 模型,用于早期发现传染病. 基于图形的ML模型显著提高了诊断流感和肝炎的准确性.
科学领域:
- 传染病流行病学 传染病流行病学
- 医疗信息学医学信息学
- 在医疗保健中的数据科学.
背景情况:
- 传染病由于传播迅速,对公共健康构成重大风险.
- 目前的早期检测方法缺乏统一的,可互操作的模型.
- 需要一个整体的实时系统,用于初步检测传染病.
研究的目的:
- 开发一个整体的实时模型,用于快速,初步检测传染病.
- 利用医学知识图 (MKG) 来分析与疾病相关的数据.
- 提高机器学习 (ML) 模型的性能,以诊断疾病.
主要方法:
- 构建了一个医学知识图 (MKG),集成多个数据源和本体学.
- 应用图形算法用于特征提取和分析.
- 评估和优化机器学习模型,特别是多层感知器 (MLP) 和随机森林 (RF),用于疾病检测.
主要成果:
- 基于图形的MLP和RF模型表现出优越的性能,损失和错误率最小.
- 在回忆,精度和F1指标方面取得了高分,具有最佳的马修斯相关系数.
- 在检测流感和肝炎方面表现优于现有的ML技术.
结论:
- 基于图形的MLP和RF模型成功诊断出流感和肝炎.
- 展示了图形数据科学的潜力,以提高ML模型性能.
- 强调了MKG揭示隐藏关系以提高疾病检测的能力.
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