基于实用工具的统计方法和深度学习模型的分析,用于合成数据生成,重点是关联结构:算法开发和验证
Marko Miletic1, Murat Sariyar1
1Institute for Optimisation and Data Analysis (IODA), Bern University of Applied Sciences, Biel, Switzerland.
JMIR AI
|March 20, 2025
概括
像synthpop这样的统计方法在合成医疗数据生成方面优于深度学习,从而保持了数据的实用性. 包括LLM在内的深度学习模型显示出混合的结果,特别是在较小的数据集.
科学领域:
- 医学数据科学 医学数据科学
- 医疗保健中的机器学习
- 合成数据生成 合成数据生成
背景情况:
- 生成对抗网络和LLM的进步使医学数据合成成为可能.
- 深度学习方法为高质量,现实的数据集提供了潜力,这对医疗保健至关重要.
- 在医疗数据集中准确捕捉复杂的关联仍然存在挑战.
研究的目的:
- 评估各种合成数据生成 (SDG) 方法来复制医疗数据集相关性结构.
- 使用随机森林和其他模型评估下游任务的SDG方法性能.
- 比较统计 (synthpop,copula) 和深度学习 (ctgan,tvae,LLMs) 的可持续发展目标方法.
主要方法:
- 在模拟和现实世界医疗数据集 (身体表现,乳腺癌,糖尿病) 上评估了SDG方法.
- 使用相关性矩阵,倾向性得分MSE (pMSE) 和F1得分评估数据质量.
- 通过对合成数据的培训模型和对真实数据的测试,比较合成数据的实用性.
主要成果:
- 在保持相关性结构方面,统计方法 (synthpop,copula) 始终优于深度学习方法.
- 总体而言,Synthpop是最有效的可持续发展目标方法.
- 包括LLM在内的深度学习方法显示出可变的性能,与数值依赖和较小的数据集作斗争.
结论:
- 统计方法,特别是synthpop,对于合成表格数据生成更优越,提供了稳定性和实用性.
- 科普拉方法看起来很有前途,但对整数变量有局限性.
- 深度学习方法在一般表式合成数据中表现不佳,但可能有利基应用.
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