Prognosis of Body Fluid Level by Fuzzy Logic Technique

Abstract

Surgical fluid replacement is a critical issue in medicine as the fluid volume excess or deficit can both complicate the patient’s condition. Currently, the administration of fluid volume is carried out primarily based on the experience and expertise of the anaesthetist as there is no analytical method available to estimate the patient’s fluid level. The development of a decision support system (DSS) to assist the anaesthetist in estimating the required fluid infusion rate for a particular patient has been the focus of this research paper. The DSS is developed based on Fuzzy Logic Control (FLC) technique which is ideal for developing input/output models in an unstructured and/or complex environment. The fuzzy rules used in the DSS are derived automatically from the clinical data produced in surgical operations. The DSS employs a Multi Rule Base (MRB) learning scheme to adapt its model according to the significant variation in the physiological parameters of a patient. The performance of the developed algorithms is validated through experimental work using clinical data. The results obtained so far are encouraging.

Keywords

Fuzzy Logic Control - Decision Support System - Multi Rule Base

• References

• 1 Klein M, Methlie LB. Expert systems, a Decision Support Approach. England: Addison-Wesley Publishers Ltd.; 1990: 147-221.
• 2 Ying H, McEachern M, Eddlwman DW, Sheppard LC. Fuzzy Control of Mean Arterial Pressure in Postsurgical Patients with Sodium Nitroprusside Infusion. IEEE Transactions on Biomedical Engineering 1992; 39 (Suppl. 10) 1060-70.
• 3 Isaka S, Sebald AV, Smith NT, Quinn ML. A Fuzzy Blood Pressure Controller. Proceedings of the 10th Annual International Conference of IEEE Engineering in Medicine and Biology Society 1988; 3: 1410-1.
• 4 Mason DG, Linkens DA, Edwards ND, Reilly CS. Automated Drug Delivery in Muscle Relaxant Anaesthesia Using Self-Organizing Fuzzy Logic Control. Proceedings of the 16 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1994; 2: 1408-9.
• 5 Nebot A, Cellier FE, Linkens DA. Synthesis of an anaesthetic agent administration system using fuzzy inductive reasoning. Art Intell Med 1996; 8: 147-66.
• 6 Lee CC. Fuzzy Logic in Control Systems: Fuzzy Logic Controller Part 1. IEEE Transactions on Systems, Man and Cybernetics 1990; 20: 404-18.
• 7 Silva Neto JL, Hoang LH. A Fuzzy Rule Changing Algorithm for an Adaptive fuzzy Controller. Proceedings of the 1997 IEEE International Symposium on Industrial Electronics 1997; 3: 1157-61.
• 8 Manly BFJ. Multivariate statistical methods: a primer. London: Chapman and Hall; 1994: 8.
• 9 Wang LX, Mendel JM. Generating Fuzzy Rules by Learning From Examples. Proceedings of the 1991 IEEE International Symposium on the Intelligent Control. August 1991: 263-8.