Summary
Objective:
We have been developing a decision support system that uses electronic clinical data and provides alerts to clinicians. However, the inference rules for such a system are difficult to write in terms of representing domain concepts and temporal reasoning. To address this problem, we have developed an ontologybased mediator of clinical information for the decision support system.
Methods:
Our approach consists of three steps: 1) development of an ontology-based mediator that represents domain concepts and temporal information; 2) mapping of clinical data to corresponding concepts in the mediator; 3) temporal abstraction that creates high-level, interval-based concepts from time-stamped clinical data. As a result, we can write a concept-based rule expression that is available for use in domain concepts and interval-based temporal information. The proposed approach was applied to a prototype of clinical alert system, and the rules for adverse drug events were executed on data gathered over a 3-month period.
Results:
The system generated 615 alerts. 346 cases (56%) were considered appropriate and 269 cases (44%) were inappropriate. Of the false alerts, 192 cases were due to data inaccuracy and 77 cases were due to insufficiency of the temporal abstraction.
Conclusion:
Our approach enabled to represent a concept-based rule expression that was available for the prototype of a clinical alert system. We believe our approach will contribute to narrow the gaps of information model between domain concepts and clinical data repositories.
Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification