Domínguez, Jesús; Prociuk, Denys; Marović, Branko; Čyras, Kristijonas; Cocarascu, Oana; Ruiz, Francis; Mi, Ella; Mi, Emma; Ramtale, Christian; Rago, Antonio; +4 more... Darzi, Ara; Toni, Francesca; Curcin, Vasa; Delaney, Brendan; (2024) ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines. Learning health systems, 8 (2). e10391. ISSN 2379-6146 DOI: https://doi.org/10.1002/lrh2.10391
Permanent Identifier
Use this Digital Object Identifier when citing or linking to this resource.
Abstract
INTRODUCTION: 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. In this work, we 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 Serbia, a middle-income country in West Balkans. 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 chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. 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 was suggested in the future along with customisation of the level of explanation with expertise. 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.
Item Type | Article |
---|---|
Faculty and Department | Faculty of Public Health and Policy > Dept of Global Health and Development |
PubMed ID | 38633019 |
Elements ID | 208867 |
Official URL | http://dx.doi.org/10.1002/lrh2.10391 |
Download
Filename: ROAD2H Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity a.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Download