  Many diseases, including cancer and chronic conditions, require extended
treatment periods and long-term strategies. Machine learning and AI research
focusing on electronic health records (EHRs) have emerged to address this need.
Effective treatment strategies involve more than capturing sequential changes
in patient test values. It requires an explainable and clinically interpretable
model by capturing the patient's internal state over time.
  In this study, we propose the "deep state-space analysis framework," using
time-series unsupervised learning of EHRs with a deep state-space model. This
framework enables learning, visualizing, and clustering of temporal changes in
patient latent states related to disease progression.
  We evaluated our framework using time-series laboratory data from 12,695
cancer patients. By estimating latent states, we successfully discover latent
states related to prognosis. By visualization and cluster analysis, the
temporal transition of patient status and test items during state transitions
characteristic of each anticancer drug were identified. Our framework surpasses
existing methods in capturing interpretable latent space. It can be expected to
enhance our comprehension of disease progression from EHRs, aiding treatment
adjustments and prognostic determinations.
