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Autori principali: Murris, Juliette, Amadei, Tristan, Kirscher, Tristan, Klein, Antoine, Tropeano, Anne-Isabelle, Katsahian, Sandrine
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2403.03138
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author Murris, Juliette
Amadei, Tristan
Kirscher, Tristan
Klein, Antoine
Tropeano, Anne-Isabelle
Katsahian, Sandrine
author_facet Murris, Juliette
Amadei, Tristan
Kirscher, Tristan
Klein, Antoine
Tropeano, Anne-Isabelle
Katsahian, Sandrine
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_03138
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A novel methodological framework for the analysis of health trajectories and survival outcomes in heart failure patients
Murris, Juliette
Amadei, Tristan
Kirscher, Tristan
Klein, Antoine
Tropeano, Anne-Isabelle
Katsahian, Sandrine
Methodology
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.
title A novel methodological framework for the analysis of health trajectories and survival outcomes in heart failure patients
topic Methodology
url https://arxiv.org/abs/2403.03138