使用不完整的多模式数据进行预测分析的新型转移学习模型
Xiaonan Liu1, Kewei Chen2, David Weidman2
1Industrial Engineering, Arizona State University, Tempe, AZ, USA.
IISE transactions
|July 3, 2023
概括
本研究介绍了不完整的多模式转移学习 (IMTL),以有效地使用不完整的数据集进行预测分析. IMTL提高了诊断的准确性,特别是在使用成像数据早期检测阿尔茨海默氏症时.
科学领域:
- 机器学习 机器学习
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 多模式数据集提供补充信息,但往往有缺失的数据,创建不完整的多模式数据集 (IMD).
- 融合不完整的多模式数据对预测分析提出了重大挑战.
- 由于成本和可访问性,现有的方法与普遍不可用的模式作斗争.
研究的目的:
- 为IMD进行预测分析提出一种新的不完整多模转移学习 (IMTL) 模型.
- 为了使不同缺失的模式模式的子群体能够跨越学习转移.
- 为了提高像早期阿尔茨海默氏症 (AD) 这样的疾病的诊断和预后准确度.
主要方法:
- 开发了一个不完整的多模式转移学习 (IMTL) 模型.
- 使用预期最大化 (EM) 算法进行参数估计.
- 将IMTL扩展为保护隐私的协作学习范式.
主要成果:
- IMTL 证明了其在样本之外进行预测的能力.
- 与非转移学习模型相比,对于更大的费舍尔信息的理论保证已被证明.
- 应用到轻度认知障碍 (MCI) 诊断和预后使用不完整的成像数据,IMTL实现了更高的准确性.
结论:
- IMTL有效地处理不完整的多模式数据集,以改善预测建模.
- 该模型为医疗应用提供了理论上的优势和准确性的实际改进.
- 在早期诊断和预后方面,IMTL显得有前途,特别是在隐私问题在医疗保健领域.
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