  Deep learning has demonstrated success in health risk prediction especially
for patients with chronic and progressing conditions. Most existing works focus
on learning disease Network (StageNet) model to extract disease stage
information from patient data and integrate it into risk prediction. StageNet
is enabled by (1) a stage-aware long short-term memory (LSTM) module that
extracts health stage variations unsupervisedly; (2) a stage-adaptive
convolutional module that incorporates stage-related progression patterns into
risk prediction. We evaluate StageNet on two real-world datasets and show that
StageNet outperforms state-of-the-art models in risk prediction task and
patient subtyping task. Compared to the best baseline model, StageNet achieves
up to 12% higher AUPRC for risk prediction task on two real-world patient
datasets. StageNet also achieves over 58% higher Calinski-Harabasz score (a
cluster quality metric) for a patient subtyping task.
