  In healthcare prediction tasks, it is essential to exploit the correlations
between medical features and learn better patient health representations.
Existing methods try to estimate feature correlations only from data, or
increase the quality of estimation by introducing task-specific medical
knowledge. However, such methods either are difficult to estimate the feature
correlations due to insufficient training samples, or cannot be generalized to
other tasks due to reliance on specific knowledge. There are medical research
revealing that not all the medical features are strongly correlated. Thus, to
address the issues, we expect to group up strongly correlated features and
learn feature correlations in a group-wise manner to reduce the learning
complexity without losing generality. In this paper, we propose a general
patient health representation learning framework MedFACT. We estimate
correlations via measuring similarity between temporal patterns of medical
features with kernel methods, and cluster features with strong correlations
into groups. The feature group is further formulated as a correlation graph,
and we employ graph convolutional networks to conduct group-wise feature
interactions for better representation learning. Experiments on two real-world
datasets demonstrate the superiority of MedFACT. The discovered medical
findings are also confirmed by literature, providing valuable medical insights
and explanations.
