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.

Over the past few years, the field of artificial intelligence (AI) has shown great promise in various domains, including medicine. A potential use case for AI in medicine is its application in managing advanced-stage cancer treatment, where limited evidence often makes treatment choices reliant on the personal expertise of the physicians. The complex nature of oncological disease processes and the multitude of factors that need to be considered when making treatment decisions make it difficult to rely solely on evidence-based trial data, which is often limited and may exclude certain patient populations. This results in physicians making decisions on a case-by-case basis, drawing on their experience of previous cases, which is not always objective and may be limited by the small number of cases they have observed. In this context, the use of clinical decision support systems (CDSS) using similaritybased AI approaches can potentially contribute to better oncology treatment by supporting physicians in the selection of treatment methods [1, 2]. One approach is Case-Based Reasoning (CBR), a subfield of AI that deals with experience-based problem solving.