🤖 AI Summary
Clinical pathway modeling traditionally relies on manual design and struggles to adapt to disease variants and comorbidities. To address this, we propose a two-stage process mining framework: (1) automatic discovery of process models from electronic health records (using Synthea-simulated SARS-CoV-2 data) via algorithms such as Heuristic Miner; and (2) dynamic expansion of the clinical pathway knowledge base through conformance checking, enabling subtype- and comorbidity-aware fine-grained modeling. Our key contribution lies in tightly coupling process mining with iterative, feedback-driven knowledge base updates—balancing real-world practice diversity with model interpretability. Experimental evaluation demonstrates that our method achieves 95.62% AUC in pathway identification and 67.11% arc simplicity, significantly outperforming static modeling approaches.
📝 Abstract
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%.