关于实体嵌入的正数特征作为代表性学习的复发预测尿膀癌的预测
Louisa Schwarz1,2, Franz Rothlauf1
1Johannes Gutenberg University, Mainz, Germany.
Studies in health technology and informatics
|August 23, 2024
概括
实体嵌入编码显著提高了机器学习模型的准确性,用于预测尿膀癌 (UBC) 复发. 这种方法提供了序列特征的优越表示,比传统编码技术提高了预测质量.
科学领域:
- 在瘤学瘤学.
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 尿路膀癌 (UBC) 的复发受到各种因素的影响,这构成了重大的临床挑战.
- 机器学习 (ML) 在预测UBC复发方面表现有前途,通常表现优于传统方法.
- 机器学习算法需要数字输入,需要对分类数据进行有效编码.
研究的目的:
- 评估不同编码策略对UBC复发的ML预测质量的影响.
- 为了比较一热,序列和实体嵌入用于编码序列特征.
主要方法:
- 人工神经网络被用来预测2年的UBC复发.
- 我们比较了三个编码策略 (一热,顺序,实体嵌入).
- 使用了来自莱兰-柏林州癌症登记处的数据.
主要成果:
- 实体嵌入实现了卓越的预测质量,精度为84.6%,准确度为73.8%.
- 该模型在测试数据上显示了68.9%的AUC.
- 实体嵌入性能优于一热和顺序编码.
结论:
- 实体嵌入编码提供了一个更准确的序列特征的数值表示.
- 这种增强的表示导致了UBC复发的更好的概括性和预测质量.
- 实体嵌入建议用于处理瘤学中的顺序分类数据的ML模型.
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