title: Thomas: building Bayesian statistical expert systems to aid in clinical decision making.
abstract: Knowledge-based system for classical statistical analysis must separate the task of analyzing data from that of using the results of the analysis. In contrast, a Bayesian framework for building biostatistical expert system allows for the integration of the data-analytic and decision-making tasks. The architecture of such a framework entails enabling the system (1) to make its recommendations on decision-analytic grounds; (2) to construct statistical models dynamically; (3) to update a statistical model based on the user's prior beliefs and on data from, the methodological concerns evinced by, the study. This architecture permits the knowledge engineer to represent a variety of types of statistical and domain knowledge. Construction of such systems requires that the knowledge engineer reinterpret traditional statistical concerns, such as by replacing the notion of statistical significance with that of a pragmatic clinical threshold. The clinical user of such a system can interact with the system at a semantic level appropriate to her fund of methodological knowledge, rather than at the level of statistical details. We demonstrate these issues with a prototype system called THOMAS which helps a physician decision maker interpret the results of a published randomized clinical trial.
authors.affiliation: Section on Medical Informatics, Stanford University School of Medicine, CA 94305-5479.
authors.forename: H P
authors.initials: HP
authors.lastname: Lehmann
authors.forename: E H
authors.initials: EH
authors.lastname: Shortliffe
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D005103:Expert Systems; D008838:Microcomputers; D015233:Models, Statistical; D012985:Software Design