  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.
