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.

本研究使用深度状态空间分析框架，通过对电子健康记录进行时间序列无监督学习，实现了对疾病发展相关的患者潜在状态的学习、可视化和聚类。通过评估癌症患者的时间序列实验室数据，我们成功发现了与预后相关的潜在状态，并通过可视化和聚类分析确定了与每种抗癌药物特征性状态转换期间患者状态和检测项目的时间转变。本框架在捕捉可解释潜在空间方面超越了现有方法，有望增加我们对电子健康记录中疾病进展的理解，有助于治疗调整和预后决策。

电子病历时间序列数据中患者潜在状态估计和分类的新深度状态空间分析框架