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Updated: Jul 29, 2025
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Using graph rewriting to operationalize medical knowledge for the revision of concurrently applied clinical practice
Martin Michalowski1, Malvika Rao2, Szymon Wilk3
1University of Minnesota, Minneapolis MN 55455, USA.
Insights
This study introduces a novel method for operationalizing secondary medical knowledge to enhance clinical practice guidelines (CPGs) for multimorbid patients. The approach uses graph rewriting to mitigate adverse interactions between CPGs, improving decision support.
Area of Science:
- Medical Informatics
- Clinical Decision Support Systems
- Knowledge Representation
Background:
- Clinical practice guidelines (CPGs) are disease-specific and lack applicability for multimorbid patients.
- Managing multimorbidity requires integrating secondary medical knowledge beyond existing CPGs.
- Operationalizing this knowledge is crucial for effective clinical implementation.
Purpose of the Study:
- To propose an approach for operationalizing secondary medical knowledge to augment CPGs.
- To develop a method for representing and applying codified medical knowledge to patient encounters.
- To formally define revisions for mitigating adverse interactions between CPGs.
Main Methods:
- Representing CPGs as task network models.
- Employing graph rewriting principles for knowledge operationalization.
- Defining formal revisions and using a vocabulary for instantiation.
Main Results:
- Demonstrated the application of the proposed approach using synthetic and clinical data.
- Successfully modeled and mitigated adverse interactions between CPGs.
- Provided a framework for integrating secondary knowledge into CPGs.
Conclusions:
- The proposed approach offers a method for enhancing CPGs for multimorbid patients.
- Further development is needed for a comprehensive theory of mitigation.
- This work paves the way for advanced decision support in managing complex patient conditions.
Abstract:
Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge coming from a variety of knowledge repositories. The operationalization of this knowledge is key to increasing CPGs' uptake in clinical practice. In this work, we propose an approach to operationalizing secondary medical knowledge inspired by graph rewriting. We assume that the CPGs can be represented as task network models, and provide an approach for representing and applying codified medical knowledge to a specific patient encounter. We formally define revisions that model and mitigate adverse interactions between CPGs and we use a vocabulary of terms to instantiate these revisions. We demonstrate the application of our approach using synthetic and clinical examples. We conclude by identifying areas for future work with the vision of developing a theory of mitigation that will facilitate the development of comprehensive decision support for the management of multimorbid patients.
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