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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.
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