Title: PASS: Phased Attentive State Space Modeling of Disease Progression Trajectories. Abstract: 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.