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AUTOMATIC POPULATION-BASED RESPONSIBILITY MODELING USING PROCESS MINING: APPLICATION TO CHRONIC OBSTRUCTIVE PULMONARY DISEASE

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  • معلومة اضافية
    • Contributors:
      Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS); Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne); Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA); Centre Ingénierie et Santé (CIS-ENSMSE); École des Mines de Saint-Étienne (Mines Saint-Étienne MSE); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP); Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)
    • بيانات النشر:
      CCSD
      IEEE
    • الموضوع:
      2025
    • الموضوع:
    • نبذة مختصرة :
      International audience ; Population-based responsibility pursues three objectives: better health and better care at a better cost. The project aims to apply this paradigm using a process mining approach to build the clinical pathway of the population suffering certain disease to finally test if the process model well represents its clinical pathway. We asses our approach on a cohort of patients affected with Chronic Obstructive Pulmonary Disease. We use a national medico-administrative database of hospitalizations to extract our population, we stratify the disease and apply process mining. We propose different models with different rules to extract event logs and a design of experiments to compare the models using quantitative indicators: fitness, precision, generalization, simplicity and replicability through simulation. We also propose a qualitative evaluation of the best models following medical expert opinion. Our approach confirms that the models well represent the medical records and the simulation partially replicates them.
    • الرقم المعرف:
      10.1109/WSC63780.2024.10838908
    • الدخول الالكتروني :
      https://hal.science/hal-04973269
      https://hal.science/hal-04973269v1/document
      https://hal.science/hal-04973269v1/file/WSC2024_v5.pdf
      https://doi.org/10.1109/WSC63780.2024.10838908
    • Rights:
      http://hal.archives-ouvertes.fr/licences/copyright/ ; info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.E279679B