Heart failure (HF) contributes to circa 200,000 annual hospitalizations in France. With the increasing age of HF patients, elucidating the specific causes of inpatient mortality became a public health problematic. We introduce a novel methodological framework designed to identify prevalent health trajectories and investigate their impact on death. The initial step involves applying sequential pattern mining to characterize patients' trajectories, followed by an unsupervised clustering algorithm based on a new metric for measuring the distance between hospitalization diagnoses. Finally, a survival analysis is conducted to assess survival outcomes. The application of this framework to HF patients from a representative sample of the French population demonstrates its methodological significance in enhancing the analysis of healthcare trajectories.

翻译：心力衰竭（HF）在法国每年导致约20万例住院病例。随着心衰患者年龄的增长，明确住院死亡的具体原因已成为公共卫生领域的重要课题。我们提出了一种新型方法论框架，旨在识别常见健康轨迹并探究其对死亡的影响。该框架首先通过序列模式挖掘技术表征患者轨迹，继而基于衡量住院诊断间距的新指标实施无监督聚类算法，最终开展生存分析以评估生存结局。将该框架应用于法国代表性人群样本中的心衰患者，验证了其在增强医疗轨迹分析方面的方法论价值。