A knowledge-based clinical decision support system for personalized health examination items in China: design and evaluation.

Background: Health examination identifies risk factors and diseases at an early stage through a series of health examination items. In China, however, the incidence of consulting services for health examination items is low and the current health examination item package is insufficiently personalized. Therefore, we created and evaluated a clinical decision support system (CDSS) for personalized health examination items.

Methods: An ontology with the data properties as the core design was created to guide the knowledge expression. A knowledge graph composed of ontology-guided property graphs was developed to provide rich and clear decision-making knowledge. The system, including the web for primary care clinicians and the app for participants, was constructed to directly assist primary care clinicians through personalized and interpretable health examination item recommendations. The enter rate and mapping rate were created to evaluate the system's capability to process input health feature data. The two-step expert evaluation was designed to assess whether recommendations with several health examination items were appropriate for participants. The system recommendations and existing packages were compared to the expert's gold standard.

Results: There were 15 classes, 2-level class hierarchies, 3 types of object properties, and 16 types of data properties in the health examination item recommendation ontology. Several different data properties could express a piece of complex decision-making knowledge and reduce the number of classes. There were 584 classes, 781 object properties, and 1094 data properties in the knowledge graph. Retrospective data from 70 participants, with a total of 472 health features, were selected for system evaluation. The ontology can cover 96.2% of the health features. 56.4% health features entered into the system had corresponding health examination items. The precision and recall of the system were 96.3% and 84.8%, and the packages were 72.5% and 69.1%.

Conclusions: The performance of this system was close to experts and outperformed the current impersonalized health examination item packages. This system could improve the personalization of health examination items and the health examination consultation services, and promote participants' engagement in the health examination.