Development and evaluation of an open-source, standards-based approach to explainable artificial intelligence for managing co-morbidity and clinical guidelines using argumentation techniques and the Transition-based Medical Recommendation model.

I. Abstract A. Objective Clinical Decision Support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. We aimed to develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial Electronic Health Record (EHR) system in a middle-income country. B. Materials and Methods We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage COPD with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. C. Results Pulmonologists agreed 97% of the time with the GOLD based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities were suggested in the future along with customisation of the level of explanation with expertise. D. Conclusion An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further. E. Funding The project was funded by the British government through the Engineering and Physical Sciences Research Council (EPSRC): Global Challenges Research Fund.