Identifying the relationship between healthcare attributes, lifestyles, and personality is vital for understanding and improving physical and mental conditions. 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 attributes about healthcare, lifestyles, and personalities. 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 1,800 attributes efficiently. We numerically evaluate the performance of VHGM and its training techniques. As a proof-of-concept of VHGM, we present several applications demonstrating user scenarios, such as virtual measurements of healthcare attributes and hypothesis verifications of lifestyles.

翻译：识别医疗健康属性、生活方式与人格之间的关联对于理解和改善身心健康至关重要。机器学习方法有望通过建模这些关联并提供可操作建议。本文提出虚拟人生成模型（VHGM），这是一种用于估计医疗健康、生活方式及人格属性的机器学习模型。VHGM是一种深度生成模型，通过掩码建模训练学习在已知条件下属性的联合分布。利用异构表格数据集，VHGM高效学习了超过1,800种属性。我们通过数值实验评估了VHGM及其训练技术的性能。作为概念验证，我们展示了若干应用场景，例如医疗健康属性的虚拟测量与生活方式假设验证。