Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach

TL;DR

This paper introduces a neuro-symbolic method using Logic Tensor Networks to incorporate domain-specific process constraints into predictive process monitoring, improving accuracy and compliance.

Contribution

It presents a novel neuro-symbolic approach that integrates process knowledge into predictive models, enhancing both compliance and prediction accuracy.

Findings

The approach learns process constraints effectively.

It achieves higher compliance in predictions.

It outperforms baseline methods in accuracy.

Abstract

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.