用医学分类体系提升治疗步骤预测准确率
Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment
- 结合疾病与手术编码的分类体系，通过图匹配计算代码相似性
- 在MIMIC-IV数据集上显著提升下一步治疗预测性能
- 适合医疗决策支持系统研发者参考
现代医学快速发展给治疗方案制定带来挑战。利用预测业务流程监控中的下一步活动预测（NAP）技术，可辅助医生规划治疗路径。现有电子健康记录数据虽可用于推荐下一步治疗，但受限于医学知识密集、个体差异大及数据稀缺等问题。本文提出TS4NAP方法，融合国际疾病分类第十版临床修改版（ICD-10-CM）和手术操作分类（ICD-10-PCS）的医学分类体系，通过图匹配分析医疗编码间的语义相似性，以预测最可能的下一步治疗。实验基于MIMIC-IV数据集中的事件日志进行评估，结果表明，利用领域知识可有效提升预测准确性，并增强预测结果的可解释性，有助于改善临床决策支持。
原文摘要 · Abstract (English)
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.
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