  We propose a causal hidden Markov model to achieve robust prediction of
irreversible disease at an early stage, which is safety-critical and vital for
medical treatment in early stages. Specifically, we introduce the hidden
variables which propagate to generate medical data at each time step. To avoid
learning spurious correlation (e.g., confounding bias), we explicitly separate
these hidden variables into three parts: a) the disease (clinical)-related
part; b) the disease (non-clinical)-related part; c) others, with only a),b)
causally related to the disease however c) may contain spurious correlations
(with the disease) inherited from the data provided. With personal attributes
and the disease label respectively provided as side information and
supervision, we prove that these disease-related hidden variables can be
disentangled from others, implying the avoidance of spurious correlation for
generalization to medical data from other (out-of-) distributions. Guaranteed
by this result, we propose a sequential variational auto-encoder with a
reformulated objective function. We apply our model to the early prediction of
peripapillary atrophy and achieve promising results on out-of-distribution test
data. Further, the ablation study empirically shows the effectiveness of each
component in our method. And the visualization shows the accurate
identification of lesion regions from others.
