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Process mining and machine learning models are playing a vital role in the medical field today. These models are enabling the development of new technologies that improve the study of hospital processes. The focus of this work is on optimally reconstructing a process that has defects such as missing activity labels. In order to achieve this, a prediction is made on the activities present in hospital processes, exploring the previous and subsequent activities in an ordered trace. The experiments were conducted on real data that refer to hospital event logs.

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Repairing Missing Activity Labels in Healthcare Process Logs: a Machine Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Lerina Aversano,
  • Martina Iammarino,
  • Antonella Madau,
  • Debora Montano,
  • Chiara Verdone

摘要

Process mining and machine learning models are playing a vital role in the medical field today. These models are enabling the development of new technologies that improve the study of hospital processes. The focus of this work is on optimally reconstructing a process that has defects such as missing activity labels. In order to achieve this, a prediction is made on the activities present in hospital processes, exploring the previous and subsequent activities in an ordered trace. The experiments were conducted on real data that refer to hospital event logs.