用流程挖掘自动发现并扩展临床路径，提升诊疗标准化与个性化适配能力。
Adaptive Identification and Modeling of Clinical Pathways with Process Mining
- 基于历史治疗数据构建初始流程模型，再通过合规性检查识别偏差
- 在新冠并发症数据集上实现95.62%的AUC准确率，模型复杂度保持在67.11弧度
- 适合医疗决策支持系统研发者及临床路径优化研究者使用
临床路径是用于规范患者治疗流程的医疗计划，旨在通过基于标准的进展路径提高护理质量、减少资源消耗并加速康复。然而，仅依赖临床指南和专家经验进行人工建模存在困难，且难以反映不同疾病组合下的实际最佳实践。本文提出一种两阶段流程挖掘方法，通过合规性检查诊断来扩展临床路径的知识库。第一阶段收集特定疾病的病史数据，生成治疗流程模型；第二阶段将新数据与参考模型比对，验证其合规性。根据合规性结果，可补充针对新疾病变体或组合的精细化模型。我们在模拟新冠病毒感染及其并发症的Synthea基准数据集上验证了该方法，结果显示该方法能在保持67.11弧度简单性的前提下，使知识库扩展达到95.62%的最高AUC，具备高精度与可解释性。
原文摘要 · Abstract (English)
Clinical pathways are specialized healthcare plans that model patient treatment procedures. They are developed to provide criteria-based progression and standardize patient treatment, thereby improving care, reducing resource use, and accelerating patient recovery. However, manual modeling of these pathways based on clinical guidelines and domain expertise is difficult and may not reflect the actual best practices for different variations or combinations of diseases. We propose a two-phase modeling method using process mining, which extends the knowledge base of clinical pathways by leveraging conformance checking diagnostics. In the first phase, historical data of a given disease is collected to capture treatment in the form of a process model. In the second phase, new data is compared against the reference model to verify conformance. Based on the conformance checking results, the knowledge base can be expanded with more specific models tailored to new variants or disease combinations. We demonstrate our approach using Synthea, a benchmark dataset simulating patient treatments for SARS-CoV-2 infections with varying COVID-19 complications. The results show that our method enables expanding the knowledge base of clinical pathways with sufficient precision, peaking to 95.62% AUC while maintaining an arc-degree simplicity of 67.11%.
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