Multistage Fuzzy Inference for Blood Pressure Control in Anesthesia and Knowledge Extraction from Time-Series Clinical Data by GA

• Published in: JOURNAL OF JAPAN SOCIETY FOR FUZZY THEORY AND SYSTEMS Vol. 14; no. 2; pp. 228 - 239
• Main Authors: UKAI, Hidetoshi, OGURA, Hisakazu, OSHITA, Shuzo, SHIRAI, Haruhiko, ODAKA, Tomohiro, NISHINO, Junji
• Format: Journal Article
• Language: English; Japanese
• Published: Japan Society for Fuzzy Theory and Intelligent Informatics 15.04.2002
• Subjects: Genetic algorithm; Knowledge aquistion; Knowledge expression; Multistage Fuzzy Inference
• ISSN: 0915-647X; 2432-9932; 2432-9932
• DOI: 10.3156/jfuzzy.14.2_228

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.