[2503.07638] Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment

Computer Science > Machine Learning arXiv:2503.07638 (cs) [Submitted on 5 Mar 2025 (v1), last revised 4 Mar 2026 (this version, v3)] Title:Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment Authors:Martin Kuhn, Joscha Grüger, Tobias Geyer, Ralph Bergmann View a PDF of the paper titled Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment, by Martin Kuhn and 3 other authors View PDF HTML (experimental) Abstract: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 treat...