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.
翻译:调查特征和表达方式之间的因果关系在医疗保健分析中起着关键作用。 使用特定特征的表达方式的有效合成能够极大地促进健康风险管理和医疗决策。例如,预测特定治疗特征给患者带来的生理症状有助于疾病预防和个性化治疗战略设计。因此,本研究的目标是根据特定特征有效地综合表达方式。然而,从特征到表达方式的绘图通常从相对较低的维度空间到高维空间,但多数现有方法,如回归模型无法有效地处理此类绘图。此外,特征和表达方式之间的关系可能不仅包括确定性模式,而且还包括随机模式。为了应对这些挑战,本文提议了一种新的选择性共同特征到表达综合(SE-CTES)方法,该方法的灵感来自感化对抗网络(GAN),而从从特征到表达方式的图示图解通常分为三个方面:(1) 以GAN为基础的深层神经网络结构被纳入到包含确定性和随机性模式的高维度制图,此外,还可能包含确定性和随机性模式,还有随机性模式。 为了应对这些挑战,本文件提议采用的新选择性综合方法,拟议进行两种方法的精确性研究的比重。