一个混合PSO-AVOA框架,用于患者报告的药物优先级,并加强探索和利用
Manickavasagam Suruthi1, Narayanan Ganesh1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Frontiers in digital health
|November 28, 2025
概括
一个新的混合粒子群增强非洲优化算法 (混合PSO-EAVOA) 通过分析患者评论有效地优先考虑药物. 这种计算方法在药物发现和药物监督应用中提供了卓越的性能.
科学领域:
- 计算药物发现和药物监测.
背景情况:
- 患者生成的药物评价是计算药物优先级的宝贵资源.
- 现有的方法可能缺乏在复杂的优化任务中平衡勘探和开发的效率.
研究的目的:
- 开发一种先进的优化算法,即混合粒子群增强的非洲优化算法 (混合PSO-EAVOA),用于分析患者生成的药物评价.
- 为了提高药物优先级,在优化中加强探索和利用之间的平衡.
主要方法:
- 开发了混合PSO-EAVOA,结合了基于反对的学习,强征飞行和精英保护.
- 将多个评估标准 (有效性,副作用,一致性) 集成到一个单一目标的优化方案中,使用加权总和适应性函数.
- 在Kaggle的大规模药物审查数据集上验证了算法.
主要成果:
- 混合PSO-EAVOA与五种最先进的元启发算法相比,显示出更高的融合速度,稳定性和解决方案质量.
- 该算法有效地处理了各种药物审查数据的临床意义.
结论:
- 混合PSO-EAVOA显示出在药物监测,临床决策支持和药物重新用途方面的应用潜力显著.
- 开发的框架可适应其他聚合方法,增强其多功能性.
- 该研究提供了公开可用的数据集和源代码,用于可复制性和进一步研究.
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