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20万次住院。随着心衰患者老龄化，明确住院死亡的具体原因成为一项公共卫生难题。我们提出了一种新的方法框架，旨在识别常见的健康轨迹并研究其对死亡的影响。初始步骤涉及应用序列模式挖掘来表征患者轨迹，随后基于一种用于衡量住院诊断之间距离的新指标进行无监督聚类算法。最后，进行生存分析以评估生存结局。将该框架应用于来自法国代表性样本的心衰患者，证明了其在增强医疗轨迹分析方面的方法学意义。