A framework for distributed mediation of temporal-abstraction queries to clinical databases.
OBJECTIVE
The specification and creation of a distributed system that integrates medical knowledge bases with time-oriented clinical databases; the goal is to answer complex temporal queries regarding both raw data and its abstractions, such as are often required in medical applications.


METHODS
(1) Specification, design, and implementation of a generalized access method to a set of heterogeneous clinical data sources, by using a virtual medical-record interface and by mapping the local terms to a set of standardized medical vocabularies; (2) specification of a generalized interface to a set of knowledge sources; (3) specification and implementation of a service, called ALMA that computes complex time-oriented medical queries that include both raw data and abstractions derivable from it; (4) design and implementation of a mediator, called IDAN, that answers raw-data and abstract queries by integrating the appropriate clinical data with the relevant medical knowledge and uses the computation service to answer the queries; (5) an expressive language that enables definition of time-dependent medical queries, which are referred to the mediator; (6) evaluation of the effect of the system, when combined with a new visual interface, called KNAVE-II, on the speed and accuracy of answering a set of complex queries in an oncology sub domain, by a group of clinicians, compared to answering these queries using paper or an electronic spreadsheet.


RESULTS
We have implemented the full IDAN architecture. The IDAN/KNAVE-II combination significantly increased the accuracy and speed of answering complex queries about both the data and their abstractions, compared to the standard tools.


CONCLUSIONS
The implemented architecture proves the feasibility of the distributed integration of medical knowledge sources with clinical data of heterogeneous sources. The results suggest that the proposed IDAN modular architecture has potential significance for supporting the automation of clinical tasks such as diagnosis, monitoring, therapy, and quality assessment.
Boaz
David
D
Department of Information Systems Engineering, Ben-Gurion University, Beer Sheva 84105, Israel. dboaz@bgumail.bgu.ac.il
Shahar
Yuval
Y
D001185:Artificial Intelligence; D019992:Databases as Topic; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques