Fuzzy Expert System for Fluid Management in General Anaesthesia 256-267
*Fakher Rahim. M.Sc of Bioinformatics, Department of Bioinformatics, University of Pune. Research Center of physiology, Ahwaz Jondishapur University of medical sciences. Tel: 00989163102183; e-mail: firstname.lastname@example.org
Background: Fuzzy set and fuzzy logic founded by Prof. Lotfi Zadeh (1965) make it possible to define inexact medical entities as fuzzy sets and models the subjective information. Fuzzy logic is reasoning with fuzzy sets. In medicine, the contradictory natures are common facts. Anaesthetists use rules of thumb when managing patients. He adjusts the drug and fluids inflow, or possibly ventilation, to monitor physiological state of the patient. Real-world knowledge is characterised by incompleteness, inaccuracy and inconsistency. It is not possible to define precisely the terms such as high temperature, low mean arterial pressure (MAP), very high intravenous fluid rate (IFR), and alike. The field of surgery and anaesthesia is very wide as many factors contribute to it, such as diagnosis, image processing, and path physiological reasoning and anaesthesia control. Fuzzy logic seems suited to use in anaesthesia because of the way it so naturally represents the subjective human notions employed in much of medical decision making.
Patient and methods: We have selected 71 patient ASA Iâ€“II classes, aged between 15 and 50 years and weight between 40 and 85 kg. In this sequel, we have made an honest attempt to incorporate fuzzy techniques and developed a fuzzy expert system for fluid management in general anaesthesia. MAP and hourly urine output (HUO) are the fuzzy input to the fuzzy expert system as the antecedent parts of the rule and the output is the defuzzified value of IFR at the desired level.
Results: We have predicted nine different fuzzy rules by using Minâ€“Max approach, and eventually we find out the action that must be taken by using centroid approach. Then out of nine fuzzy rules four rules will be fired for patients. Based on COA, the computed value of IFR for the above set parameters, which for one sample of patient data was 118 ml/hr. Similarly, we calculated the results of fired rule for all 71 patients and got results that were in the range of predefined limit by the experts.
Conclusion: It could be done with minimal capital outlay by having a human operator periodically enter MAP and HUO values into a personal computer. The objective of the study was to estimate IFR based on the linguistic description of MAP and HUO sum of these four actions. The rates of change of MAP and HUO could be fuzzified into sets such as DECREASING, STABLE, and INCREASING and would serve to indicate the trend in a patientâ€™s fluid status. This would allow more precise control of fluid balance. Inputs from the domain experts and the judicious use of fuzzy techniques are important to achieve success. This modal is suitable for application only in otherwise healthy patients undergoing surgery involving minimal blood loss. For other patients undergoing surgeries involving moderate-to-severe blood loss, more complicated modals are needed utilising other parameters as well.