Predicting Intravenous Fluid Resuscitation Rate for Burn Patients Through Type-1 Mamdani Fuzzy Inference System Modeling

Clinical decision-making, in most of the cases, is convoluted, perplexing, and critical, and becomes more delicate in case of burn patients where specialized healthcare services are required. In an emergency situation, various factors are needed to be considered within a limited time bound to take pivotal decision, such as the rate of replacement of the bodily lost fluid due to burn. In such circumstances, a computer-aided intelligent clinical support system is doubtlessly useful to make intuitive decisions in a shortest possible time, where a fuzzy inference model (FIS) may lead to the solution. In this study, an improved FIS-based fluid resuscitation model is proposed for burn patients, where the rate of required fluid revitalization is determined intelligently. The goal of this study is to suggest an assistive model for the physician in making a quick decision for intravenous fluid rate (IFR) adjustment depending on the percentage of total body surface area (%TBSA) burned and hourly urine output (HUO) measurements in burn patients. To attain the objective, the proposed FIS model is introduced with the %TBSA and HUO as input variables and IFR as the output variable of the system. These two inputs are considered in this research due to their foundational role in classical calculations, as all existing formulas are derived from them. This study also conducts a comparative analysis among various defuzzification methods for various membership functions to determine the best possible model. The simulated results ensure the viability of proposed model for practical experimentation where centroid of area with Gaussian membership function is recommended. Furthermore, the problems of traditional estimation of IFR such as discrete rate of fluid injection and abrupt shift (≈ ±20%) of IFR level depending on the variations of HUO would be resolved by the proposed FIS model, resulting in a smooth and continuous adjustment of IFR. Nevertheless, the proposed design shows significant improvements in determining IFR, as validated through benchmarking.