BibTex format
@article{Domínguez:2024:10.1002/lrh2.10391,
author = {Domínguez, J and Prociuk, D and Marovi, B and yras, K and Cocarascu, O and Ruiz, F and Mi, E and Mi, E and Ramtale, C and Rago, A and Darzi, A and Toni, F and Curcin, V and Delaney, B},
doi = {10.1002/lrh2.10391},
journal = {Learning Health Systems},
title = {ROAD2H: development and evaluation of an open-sourceexplainable artificial intelligence approach for managingco-morbidity and clinical guidelines},
url = {http://dx.doi.org/10.1002/lrh2.10391},
volume = {8},
year = {2024}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - IntroductionClinical 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.MethodsWe 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.ResultsPulmonologists 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.ConclusionAn ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Exte
AU - Domínguez,J
AU - Prociuk,D
AU - Marovi,B
AU - yras,K
AU - Cocarascu,O
AU - Ruiz,F
AU - Mi,E
AU - Mi,E
AU - Ramtale,C
AU - Rago,A
AU - Darzi,A
AU - Toni,F
AU - Curcin,V
AU - Delaney,B
DO - 10.1002/lrh2.10391
PY - 2024///
SN - 2379-6146
TI - ROAD2H: development and evaluation of an open-sourceexplainable artificial intelligence approach for managingco-morbidity and clinical guidelines
T2 - Learning Health Systems
UR - http://dx.doi.org/10.1002/lrh2.10391
UR - https://onlinelibrary.wiley.com/doi/full/10.1002/lrh2.10391
UR - http://hdl.handle.net/10044/1/106192
VL - 8
ER -
