Computational protocol: Developing a modular architecture for creation of rule-based clinical diagnostic criteria

Similar protocols

Protocol publication

[…] The purpose here is to integrate existing standard information models relevant to modeling of diagnostic criteria by expert review and manual editing. As previously mentioned, we choose the ICD-11 content model and NQF QDM as reference information models. Our work in this stage is to create the diagnostic criteria upper ontology (DCUO) through the integration of ICD-11 content model with those QDM elements commonly used in diagnostic criteria. We evaluated the distribution of the QDM elements using a collection of textual diagnostic criteria. The selection of these QDM elements was informed by the results from a previous study []. We selected 10 QDM datatypes and 4 QDM attributes and integrated them with ICD-11 content model-based ontology schema. Table  shows a list of the QDM datatypes and attributes used for the integration. We used Protégé ontology editing environment [] for manually integrating these two standard information models into a diagnostic criteria upper ontology. We merged these two information models manually by conducting both concept and property analysis. Specifically, ICD-11 content model contributes a well-defined concept schema with general concepts that provide a systematic view of disease, whereas the QDM contributes more specific elements of disease diagnosis. Therefore, we chose all classes defined in the ICD-11 content model as our fundamental classes of DCUO, and named them by a prefix “ICD:” in conjunction with its original class name. QDM datatypes (with a prefix “QDM”) as the subclasses merged with ICD-11 content model classes. The mappings of the merged classes and integrated properties are shown in the Table . In total, four QDM attributes are integrated into the DCUO as object properties []. In addition, we also defined a number of new classes in the DCUO so that they can be used for supporting the SWRL rule construction and reasoning. Currently DCUO includes 3 newly defined classes (with a prefix “DCUO”): Patient, Unit, and Evidence. [...] After having a DCDO ontology produced, we developed JAVA-based algorithms using Protégé OWL API and SWRL API for conducting automatic rule composition and rule validation. These APIs are also responsible for rule assembling and rule grammar checking.The SWRL syntax contains two parts: Body and Head. The Body part is also called the antecedent and the Head part is the consequent of the rule. There are 7 atom types that can be used as the components of the Body and Head: class, individual property, same individual, different individual, data valued property, built-in atom and data range [].Adhering to the SWRL structure and grammar, we designed a collection of translation algorithms that automatically extract the SWRL rule elements from the logic components of an HQMF XML template and then assemble these rule elements into the SWRL syntax.For example, Table  shows the HQMF XML representation of a QDM-based criterion “Laboratory Test, Result: LDL-c (result < 100 mg/dL)”. The criterion is composed by two HQMF templates: the template “Laboratory Test, Result” (hqmf r1 template - 2.16.840.1.113883.3.560.1.12) and the template “result comparison” (hqmf r1 comparison template - 2.16.840.1.113883.3.560.1.1019.3). Our translation algorithms then automatically extract the SWRL rule elements from the logic components of the two templates and then assemble these rule elements into the SWRL syntax. The translation processing is shown in Table . Each rule is generated from an individual criterion. After each rule is composed, our algorithms could aggregate these individual rules with logic operators for the diagnosis reasoning. […]

Pipeline specifications

Software tools Protégé, OWL API
Application Ontology generation
Organisms Homo sapiens