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.

翻译：许多疾病（包括癌症和慢性病）需要长期治疗策略。针对电子健康记录（EHR）的机器学习与人工智能研究应运而生。有效的治疗方案不仅需要捕捉患者检测值的时序变化，更需要通过可解释的临床模型刻画患者内在状态随时间的变化规律。本研究提出"深度状态空间分析框架"，利用深度状态空间模型对EHR进行时间序列无监督学习。该框架能够学习、可视化和聚类与疾病进展相关的患者潜在状态时序变化。我们使用12,695名癌症患者的时序实验室数据评估该框架。通过潜在状态估计，成功发现与预后相关的潜在状态。通过可视化和聚类分析，识别出各抗癌药物特征性状态转换期间患者状态及检测项目的时序变迁。本框架在捕捉可解释潜在空间方面超越现有方法，有望提升我们从EHR理解疾病进展的能力，辅助治疗调整与预后判断。