  Disease progression models are instrumental in predicting individual-level
health trajectories and understanding disease dynamics. Existing models are
capable of providing either accurate predictions of patients prognoses or
clinically interpretable representations of disease pathophysiology, but not
both. In this paper, we develop the phased attentive state space (PASS) model
of disease progression, a deep probabilistic model that captures complex
representations for disease progression while maintaining clinical
interpretability. Unlike Markovian state space models which assume memoryless
dynamics, PASS uses an attention mechanism to induce "memoryful" state
transitions, whereby repeatedly updated attention weights are used to focus on
past state realizations that best predict future states. This gives rise to
complex, non-stationary state dynamics that remain interpretable through the
generated attention weights, which designate the relationships between the
realized state variables for individual patients. PASS uses phased LSTM units
(with time gates controlled by parametrized oscillations) to generate the
attention weights in continuous time, which enables handling
irregularly-sampled and potentially missing medical observations. Experiments
on data from a realworld cohort of patients show that PASS successfully
balances the tradeoff between accuracy and interpretability: it demonstrates
superior predictive accuracy and learns insightful individual-level
representations of disease progression.
