The rapid progress in modern medicine presents physicians with complex challenges when planning patient treatment. Techniques from the field of Predictive Business Process Monitoring, like Next-activity-prediction (NAP) can be used as a promising technique to support physicians in treatment planning, by proposing a possible next treatment step. Existing patient data, often in the form of electronic health records, can be analyzed to recommend the next suitable step in the treatment process. However, the use of patient data poses many challenges due to its knowledge-intensive character, high variability and scarcity of medical data. To overcome these challenges, this article examines the use of the knowledge encoded in taxonomies to improve and explain the prediction of the next activity in the treatment process. This study proposes the TS4NAP approach, which uses medical taxonomies (ICD-10-CM and ICD-10-PCS) in combination with graph matching to assess the similarities of medical codes to predict the next treatment step. The effectiveness of the proposed approach will be evaluated using event logs that are derived from the MIMIC-IV dataset. The results highlight the potential of using domain-specific knowledge held in taxonomies to improve the prediction of the next activity, and thus can improve treatment planning and decision-making by making the predictions more explainable.

翻译：现代医学的快速发展给医生制定患者治疗方案带来了复杂挑战。预测性业务流程监控领域的技术，如下一活动预测，可通过推荐可能的后续治疗步骤，成为支持医生制定治疗方案的有前景的技术。通过分析现有的患者数据（通常以电子健康记录的形式），可以推荐治疗过程中的下一个合适步骤。然而，由于患者数据具有知识密集型、高度可变性以及医疗数据稀缺等特点，其使用面临诸多挑战。为克服这些挑战，本文研究了利用分类法中编码的知识来改进并解释治疗过程中下一活动的预测。本研究提出了TS4NAP方法，该方法结合医学分类法（ICD-10-CM和ICD-10-PCS）与图匹配技术，通过评估医疗代码的相似性来预测下一个治疗步骤。所提方法的有效性将使用源自MIMIC-IV数据集的事件日志进行评估。研究结果突显了利用分类法中特定领域知识改进下一活动预测的潜力，从而通过使预测更具可解释性，改善治疗规划与决策制定。