相关实验视频
Updated: Jun 28, 2025

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A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
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从凝血指数的角度分析COVID-19的严重程度,使用进化机器学习与增强的脑风暴优化
Beibei Shi1,2, Hua Ye3, Ali Asghar Heidari4
1Affiliated People's Hospital of Jiangsu University, 8 Dianli Road, Zhenjiang, Jiangsu 212000, China.
概括
这项研究引入了一个AI框架,EBSO-SVM,用于使用凝血数据早期检测COVID-19的严重程度. 该模型实现了高精度,为临床分析提供了有前途的工具.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 生物化学 生物化学
背景情况:
- 准确的早期诊断和COVID-19的严重程度评估仍然具有挑战性.
- 不有效的治疗策略是由于COVID-19评估延迟或不准确导致的.
- 凝血指数为COVID-19严重程度提供了潜在的生物标志物.
研究的目的:
- 为早期识别和歧视COVID-19严重程度开发一个智能框架.
- 为了利用凝血指数来预测疾病严重程度.
- 将增强的优化算法与机器学习集成在一起,以改善分类.
主要方法:
- 为了优化,开发了一种增强的脑风暴优化算法 (EBSO) 与哈里斯霍克斯优化 (HHO).
- EBSO用于支持向量机 (SVM) 的同时参数优化和功能选择.
- 由此产生的EBSO-SVM模型是根据临床COVID-19数据进行训练和验证的,重点是凝血指数.
主要成果:
- EBSO-SVM模型显示了高分类性能:91.9%的准确性,90.5%的MCC,91.0%的灵敏性和88.6%的特异性.
- EBSO算法显示了快速的融合,并降低了局部最佳的风险.
- 在多个绩效指标上,EBSO-SVM的表现优于现有的最先进的方法.
结论:
- 拟议的EBSO-SVM框架为分析COVID-19严重程度提供了一个稳定和预测性的计算机辅助技术.
- 凝血指数是早期COVID-19严重程度评估的有价值指标.
- 智能框架为COVID-19管理中的临床决策提供了显著的优势.
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