基于生成对抗网络的选择性包围特征-表达合成（SE-CTES）方法及其在医疗保健中的应用
A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare
摘要：
调查特征和表达之间的因果关系在医疗保健分析中发挥着关键作用。利用给定的特征对表达式进行有效的综合可以为健康风险管理和医疗决策做出巨大贡献。例如，从给定的治疗特征预测患者产生的生理症状有助于疾病预防和个性化治疗策略设计。因此，本研究的目的是有效地合成基于给定特征的表达式。然而，从特征到表达式的映射通常是从一个相对低维的空间到一个高维的空间，而大多数现有的方法，如回归模型不能有效地处理这种映射。此外，特征与表达之间的关系既可能包含确定性模式，也可能包含随机性模式。为了解决这些问题，本文提出了一种受生成对抗网络（GAN）启发的选择性集成特征-表达合成（SE-CTES）方法。该方法的创新之处可以概括为三个方面：（1）引入了基于GAN的深度神经网络架构，以学习包含确定性和随机性模式的相对低维映射到高维映射;（2）提出了基于GAN的架构中两个失配误差的权重不同，以减少训练过程中的学习偏差;（3）提出了一种选择性集成学习框架，以减小预测偏差，提高综合稳定性。为了验证所提出的方法的有效性，广泛的数值模拟研究和现实世界的医疗保健案例研究，结果表明，该方法是非常有前途的。
英文摘要：
Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics. Effective synthesis for expressions using given characteristics can make great contributions to health risk management and medical decision-making. For example, predicting the resulting physiological symptoms on patients from given treatment characteristics is helpful for the disease prevention and personalized treatment strategy design. Therefore, the objective of this study is to effectively synthesize the expressions based on given characteristics. However, the mapping from characteristics to expressions is usually from a relatively low dimension space to a high dimension space, but most of the existing methods such as regression models could not effectively handle such mapping. Besides, the relationship between characteristics and expressions may contain not only deterministic patterns, but also stochastic patterns. To address these challenges, this paper proposed a novel selective ensemble characteristic-to-expression synthesis (SE-CTES) approach inspired by generative adversarial network (GAN). The novelty of the proposed method can be summarized into three aspects: (1) GAN-based architecture for deep neural networks are incorporated to learn the relatively low dimensional mapping to high dimensional mapping containing both deterministic and stochastic patterns; (2) the weights of the two mismatching errors in the GAN-based architecture are proposed to be different to reduce the learning bias in the training process; and (3) a selective ensemble learning framework is proposed to reduce the prediction bias and improve the synthesis stability. To validate the effectiveness of the proposed approach, extensive numerical simulation studies and a real-world healthcare case study were applied and the results demonstrated that the proposed method is very promising.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
