A lightweight acquisition of expert rules for interoperable clinical decision support system

Abstract

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted Manuscript version of a Published Work that appeared in final form in [Knowledge-Based Systems]. To access the final edited and published work see [10.1007/978-3-030-45385-5_9]The process of adding new knowledge in the form of rules to already running Clinical Decision Support Systems (CDSSs) in hospitals is extremely costly and time consuming. There are two principal limitations: (1) the lack of a broad consensus regarding a uniform representation of clinical rules; and (2) the integration of new rule-based knowledge into hospital information systems. Objective: To provide a guideline with which to support knowledge acquisition for rule-based CDSSs and to facilitate the integration of that knowledge into hospital datasets using standard clinical terminologies and ontologies as reference elements. Materials and Methods: We have designed a straightforward 4-step methodology with which to incorporate the external knowledge sources and data integration required to run CDSSs in hospitals. This lightweight methodology is based on a reference ontology that integrates standard clinical terminologies and its objective is to effectively acquire procedural knowledge in the form of rules. Results: We have applied the methodology in the context of antimicrobial stewardship at a hospital. Recommendations from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) were added to WASPSS, a CDSS running at the hospital. The reference ontology combines a subset of ATC terminologies for antibiotics and those of NCBI for microorganisms, including 584 and 1714 concepts, respectively. A total of 94 new rules were added to the CDSS so as to represent EUCAST knowledge. We also evaluated different implementations in order to study their scalability, during which time we analysed Drools 7.5 as a production rule engine, HermiT as an ontology reasoner and RuQAR as an integration tool. Our experiments show that the combination of a production rule engine and an ontology reasoner in runtime is more efficient than using a single rule engine with a knowledge base derived from the reference ontology (1.9 times faster than the next approach when executing 1000 expert rules on an ontology of 1000 concepts). Discussion: The methodology proposed helped to implement the knowledge acquisition process of EUCAST rules in a running CDSS. This methodology is applicable to other clinical domains when knowledge can be modelled with rules. Since it is a lightweight methodology, different implementation strategies are possible. The use of clinical standards also facilitates the future interoperation between CDSSs, particularly when using SNOMED as a reference ontology and employing future rule-sharing standards