Technique Of Artificial Intelligence For Analysing Medical Data

This paper describes a newly developed statistical expert system in medical statistics called ESGLM (Expert System for fitting Generalized Linear Models) which can run on personal computers and display with Chinese characters. There are four modules in ESGLM, those are "data management","knowledge domain", "calculation for model fitting" and "advice-giving presentation".ESGLM is based on the techniques of artificial intelligence(AI), and the design strategy is to separate "knowledge domain" of the AI system from its "inference, engine", the former consists of the facts and rules about generalized linear models (GLM), and the later is the problem-solving advices from statistical background used to guide the user for choosing the most appropriate GLM. The AI system of ESGLM is performed by Turbo Prolog language and two knowledge bases were constructed to store statistical facts and rules in logic pattern. The inference engine of ESGLM is based on the predicate logic mechanism in Turbo Prolog, and a problem-solving answer is achieved by a "hypothesis-fa(?)ts-rules matching engine" that can search the facts and rules which meet the hypothesis in the knowledge bases, show them in the window as guiding heuristics and wait for user's decision. The reasoning starts with the root of a decision tree created by the program and processes step by step. Several windows are prepared to display the statistical concepts and other heuristic knowledge in each step until the user reaches the final answer for a given question. The module for model fitting and other complex statistical computation is carried out by the program written in C language,which can be interactively operated with statistical inference if the user fills questionable on the output. The algorithm is flexible for GLM if the link function is known. For the moment EXG can treat the problems in fitting log-linear model, logistic model and linear regression model like a human expert, especially in analysis of high-dimension contingency tables by fitting log-linear model because understanding of the estimates in the fitted model is much more difficult for the users who are not familiar with medical statistics. ESGLM also supplies with a friendly operating environment,such as the prompts with Chinese* the pull-down menus, multiple windows, data edit and transformation, graphical display and so on.