Identifying the relationship between healthcare attributes, lifestyles, and personality is vital for understanding and improving physical and mental well-being. Machine learning approaches are promising for modeling their relationships and offering actionable suggestions. In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating healthcare, lifestyles, and personality attributes. VHGM is a deep generative model trained with masked modeling to learn the joint distribution of attributes conditioned on known ones. Using heterogeneous tabular datasets, VHGM learns more than 2,000 attributes efficiently. We numerically evaluate the performance of VHGM and its training techniques and have deployed VHGM as a Web service, enabling various healthcare applications.

翻译：识别健康属性、生活方式与人格特质之间的关联对于理解和改善身心健康至关重要。机器学习方法在建模这些关系并提供可操作建议方面展现出巨大潜力。本文提出虚拟人生成模型（VHGM），这是一种用于估计健康、生活方式及人格属性的机器学习模型。VHGM采用基于掩码建模训练的深度生成模型，以学习在已知属性条件下各属性的联合分布。通过使用异构表格数据集，VHGM能够高效学习超过2,000种属性。我们对VHGM及其训练技术进行了数值化性能评估，并将其部署为网络服务，以支持多样化的健康应用场景。