PhD

dissertationOne of the most severe obstacles to applying medical informatics to solve practical medical problems is acquiring the knowledge base. The Iliad knowledge base is among the most comprehensive medical knowledge bases in existence. The amount of effort devoted to its creation and maintenance is a testimony to the difficulty of building academic-quality, comprehensive knowledge bases for practical medical applications. In addition to the difficulty of knowledge acquisition, the accuracy and reliability of knowledge base are also major concerns for the developers and users of Iliad expert system. The major emphasis of this project was to use a patient database to improve the efficiency of creating a knowledge base for the neuroradiology domain and increase the accuracy of Iliad expert system based on this knowledge base. The project introduced a knowledge engineering model to automatically generate disease profiles in neuroradiology. The model used a new technique to collect patient data, obtain important statistics, calculate finding utility, and extract the best findings for the diagnostic frames. Knowledge Acquisition for a Neuroradiology Expert System (KANES) is an efficient, accurate, and easy-to-use personal computer program that can assist knowledge engineers in managing the patient database and executing the analysis tasks described above. The experience with the KANES program using a relational Database Management System (DBMS) can be extended to a larger environment where information is gathered from multiple sources and where real-time decisions need to be made. The knowledge engineering session in the Department of Medical Informatics is a good example of such an environment where different categories of individuals need and generate information and make decisions related to teaching, research, management, and administration. Under contract from the Unified Medical Language System (UMLS) project of the National Library of Medicine, a database is being built to contain clinical data from multiple sources like QMR, HELP, and Iliad. As the quality of the data in medical information systems improves, such databases will become an important resource for all of the probabilities that drive computerized diagnostic systems. The KANES program has the potential to expand to be a decision support system for all medical domains that would help the groups in the knowledge engineering session to make decisions more easily and more appropriately