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：识别医疗保健属性、生活方式与人格之间的关联，对于理解和改善个体身心健康至关重要。机器学习方法在建模其关联关系并提供可执行建议方面具有广阔前景。本文提出虚拟人类生成模型（VHGM），该机器学习模型可估计医疗保健、生活方式与人格等属性。VHGM是一种深度生成模型，通过掩码建模训练学习已知条件下的属性联合分布。利用异构表格数据集，VHGM高效学习了超过1800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，本文展示了多项应用场景，包括医疗保健属性的虚拟测量与生活方式假设验证。