Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care

In clinical practice, decision-making relies heavily on established protocols, often formalised as rules. Concurrently, machine learning (ML) models, trained on clinical data, aspire to integrate into medical decision-making processes. However, despite the growing number of ML applications, their adoption into clinical practice remains limited. Two critical concerns arise, relevant to the notions of consistency and continuity of care: (a) accuracy – the ML model, albeit more accurate, might introduce errors that would not have occurred by applying the protocol; (b) interpretability – ML models operating as black boxes might make predictions based on relationships that contradict established clinical knowledge. In this context, the literature suggests using integrated ML models to reduce errors introduced by purely data-driven approaches and improve interpretability. However, there is a lack of appropriate metrics for comparing ML models with clinical rules in addressing these challenges. Accordingly, in this article, we first propose a metric to assess the accuracy of ML models with respect to the established protocol. Secondly, we propose an approach to measure the distance of explanations provided by two rule sets, with the goal of comparing the explanation similarity between clinical rule- based systems and rules extracted from ML models. The approach is validated by employing the Pima Indians Diabetes dataset, for which a well-grounded clinical protocol is available, by training two neural networks—one exclusively on data, and the other integrating knowledge. Our findings demonstrate that the integrated ML model achieves comparable performance to that of a fully data-driven model while exhibiting superior relative accuracy with respect to the clinical protocol, ensuring enhanced continuity of care. Furthermore, we show that our integrated model provides explanations for predictions that align more closely with the clinical protocol compared to the data-driven model.