Multistage Fuzzy Inference for Blood Pressure Control in Anesthesia and Knowledge Extraction from Time-Series Clinical Data by GA - UKAI, Hidetoshi; OGURA, Hisakazu; OSHITA, Shuzo; SHIRAI, Haruhiko; ODAKA, Tomohiro; NISHINO, Junji (JOURNAL OF JAPAN SOCIETY FOR FUZZY THEORY AND SYSTEMS, 2002)
In this paper, we proposed a multistage fuzzy inference system consisted of several two-input one-output fuzzy inference engine, which infers and predicts values of a set of time-series data. The inference engine use a two dimensional fuzzy rule array to represent the time-series data production mechanism. To construct the rule arrays, we extract the knowledge of production from a real time-series data by genetic algorithm. We applied the method to extract the anesthetist's knowledge for blood pressure control in surgical operation. It was found that it is useful for data-mining to acquire the knowledge about the time-series data, automatically. The acquired knowledge as rule arrays are not sufficient because the number of time-series data available is quite restricted and these subjects needs to be investigated for future problems.