Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics

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.