Diagnostic knowledge base construction

Computer-based diagnostic expert systems in rheumatology: where do we stand in 2014?

Background. The early detection of rheumatic diseases and the treatment to target have become of utmost importance to control the disease and improve its prognosis. However, establishing a diagnosis in early stages is challenging as many diseases initially present with similar symptoms and signs. Expert systems are computer programs designed to support the human decision making and have been developed in almost every field of medicine. Methods. This review focuses on the developments in the field of rheumatology to give a comprehensive insight. Medline, Embase, and Cochrane Library were searched. Results. Reports of 25 expert systems with different design and field of application were found. The performance of 19 of the identified expert systems was evaluated. The proportion of correctly diagnosed cases was between 43.1 and 99.9%. Sensitivity and specificity ranged from 62 to 100 and 88 to 98%, respectively. Conclusions. Promising diagnostic expert systems with moderate to excellent performance were identified. The validation process was in general underappreciated. None of the systems, however, seemed to have succeeded in daily practice. This review identifies optimal characteristics to increase the survival rate of expert systems and may serve as valuable information for future developments in the field.

Building a relational database for a physician document index

We show how three existing medical knowledge bases: Medical Subject Headings (MeSH), Standardized Nomenclature of Medicine (SNOMED) and Current Medical Information and Technology (CMIT) are mapped into a relational data model and stored on an Apollo workstation and an Intelligent Database Machine. Since two of these knowledge bases have been used in the indexing of medical literature and patient records, they can be useful not only as direct views on the organization of medical concepts but also as tools for the retrieval of documents. In order that the concepts from one knowledge base can be connected to those of the other knowledge base, a method has been developed for the semi-automatic merging of MeSH, SNOMED and CMIT. This method takes advantage of the relational model and the synonyms that are given in SNOMED and CMIT, in order to recommend concepts to be merged. An expert interacts with the system to accept or reject the recommendations of the computer. The method would apply equally well to other knowledge bases and is particularly well-suited for knowledge bases that contain tens of thousands of concepts.