Dynamic Evidence Based Medicine (DEBM)is de .ned a the process of .nding evidence about the care of individual patients automatically and dynamically in the case that we cannot rely on any literatures or guidelines.In this paper,we develop a frameworkfor DEBM using data mining technologies that make it possible to automatically analyze huge clinical databases and to discover patterns behind them.We de .ne the requirements of data mining system for DEBM.The following two functions are required of the system. (1)Supporting clinical decision making,and (2)Discovering rare patterns which human beings can hardly .nd.In order to support clinical decision making,rule discovery methods such as association rule mining are applied to this framework.We adopt a post analysis approach using a rulebase and queries.Discovered rules are collected into a rulebase for further analysis.By executing queries to the rulebase,users can obtain the keys to evidence for making decisions about the clinical care.We preliminarily implement a prototype of rulebase and post analysis tool based on our framework.This tool can assist users to analyze discovered rules.