Developing a modular architecture for creation of rule-based clinical diagnostic criteria. Issue 1 (December 2016) - Hong, Na; Pathak, Jyotishman; Chute, Christopher; Jiang, Guoqian
Abstract Background With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified architecture to support the need for diagnostic criteria computerization. In this study, we present a modular architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies. Methods and results The architecture consists of two modules: an authoring module that utilizes a standards-based information model and a translation module that leverages Semantic Web Rule Language (SWRL). In a prototype implementation, we created a diagnostic criteria upper ontology (DCUO) that integrates ICD-11 content model with the Quality Data Model (QDM). Using the DCUO, we developed a transformation tool that converts QDM-based diagnostic criteria into Semantic Web Rule Language (SWRL) representation. We evaluated the domain coverage of the upper ontology model using randomly selected diagnostic criteria from broad domains (n = 20). We also tested the transformation algorithms using 6 QDM templates for ontology population and 15 QDM-based criteria data for rule generation. As the results, the first draft of DCUO contains 14 root classes, 21 subclasses, 6 object properties and 1 data property. Investigation Findings, and Signs and Symptoms are the two most commonly used element types. All 6 HQMF templates are successfully parsed and populated into their corresponding domain specific ontologies and 14 rules (93.3 %) passed the rule validation. Conclusion Our efforts in developing and prototyping a modular architecture provide useful insight into how to build a scalable solution to support diagnostic criteria representation and computerization.