Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach
This addresses the issue of limited adherence to compliance in predictive process monitoring for domains like healthcare, though it is incremental as it builds on existing neuro-symbolic methods.
The paper tackles the problem of predictive process monitoring by incorporating domain-specific constraints, such as surgery planning rules, to improve compliance and accuracy. The result is a neuro-symbolic model using Logic Tensor Networks that achieves higher compliance and improved accuracy compared to baseline approaches in compliance-aware experiments.
Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical events and biometrics. However, such approaches fail to incorporate domain-specific process constraints (knowledge), e.g., surgery can only be planned if the patient was released more than a week ago, limiting the adherence to compliance and providing less accurate predictions. In this paper, we present a neuro-symbolic approach for predictive process monitoring, leveraging Logic Tensor Networks (LTNs) to inject process knowledge into predictive models. The proposed approach follows a structured pipeline consisting of four key stages: 1) feature extraction; 2) rule extraction; 3) knowledge base creation; and 4) knowledge injection. Our evaluation shows that, in addition to learning the process constraints, the neuro-symbolic model also achieves better performance, demonstrating higher compliance and improved accuracy compared to baseline approaches across all compliance-aware experiments.