Session Program

Abstract - The domain of medical decision making process is heavily affected by vagueness and uncertainty issues and - for copying with them - different type of Clinical Decision Support System (CDSS)s, simulating human expert clinician reasoning, have been designed in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the knowledge-based CDSS actually used in the day-by-day clinical care of Beta-thalassemia patients of the Rare Red Blood Cell Disease Unit (RRBCDU) at Cardarelli Hospital (Naples, Italy). All the designed functionalities were iteratively developed on the field, through requirement-adjustment/development/validation cycles executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The paper shows exemplary results on the on-line evaluation of Iron Overload during the health status assessment and care management of Beta-Thalassemia patients.

Abstract - In the medical field determination of appropriate rate of insulin injection in order to stabilize the blood glucose to a normal level is vital for diabetics. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) based on hybrid blood glucose control data set has been presented. Hybrid blood glucose control employs combination of the fuzzy logic controller optimized by genetic algorithm with well-known Palumbo control method to regulate the blood glucose level in type-1 diabetic mellitus (T1DM) patients. Due to the complexity of the hybrid controller and non-linear and delayed nature of glucose-insulin mechanism as well as chattering phenomenon, the artificial intelligence based technique, especially the ANFIS method, is proposed in this paper. Finally, the simulation results of the fuzzy control, fuzzy-genetic control, Palumbo control and hybrid control are compared to the new proposed ANFIS control, which indicates the proper functioning of the proposed controller for tracking of desired blood glucose level at the lowest possible chattering error.

Abstract - Intensive care treatment presents unique challenges in the medical world. When treating patients, their wide variety leave care providers with few past examples to draw on. Instead of operating in a pure knowledge discovery capacity, decision support systems can be developed to help predict short- term and long-term patient outcome, based upon available data. One area in which generalized severity scoring systems have consistently performed poorly is among patients admitted to the ICU who then develop acute kidney injury. Urine output is used to guide fluid resuscitation and is one of the criteria for the diagnosis of acute kidney injury. This paper provides an example application for predicting short-term critical kidney function in an intensive care unit. Feature construction is performed to extract important aspects of the clinical evolution of the patient. Feature selection is performed on several patient features. Classifiers based on support vector machines and Takagi-Sugeno fuzzy models are developed to predict short term drops in patient urine output rate. Both types of models developed showed comparable results, with an AUC of 78{$\backslash$}\%. This shows potential in using similar classifiers to build an ICU decision support system with the goal of predicting short term complication in the patient and augment current guidelines by anticipating treatment.

Abstract - Logistic regression and Takagi-Sugeno fuzzy models are sequentially trained with categorical and numerical data in an ensemble-based multistage scheme. In the first stage, a logistic regression model is used to transform the binary feature space into a numerical feature that is used to train a second stage of models consisting of an ensemble of two Takagi-Sugeno fuzzy models. In the ensemble, one model is trained in the space of numerical features and stage 1 prediction values. The other model is trained only with samples that were classified with a low degree of confidence by the first stage model, in the space of numerical variables. The final output is given by the average of the ensemble predictions at stage 2. This scheme was devised under the hypothesis that separating binary from numerical features in the modeling process would increase the performance of a single model using both types of features together. The proposed multistage is used to solve a classification problem in a Portuguese hospital. The problem consists of predicting comanagement signalling based on patient clinical data, including diagnosis, procedures, comorbidities and numerical scores, collected before surgery. The multistage performed better in the comanagement dataset, and in 2 out of 5 benchmark datasets.

Abstract - Providing sensory innervation, the ulnar nerve runs from the shoulder to the little finger; however, when entrapped in wrist, numbness and decreased sensation would occur. One of the reason is habitual misuse of computer keyboards in harmful wrist angles, causing constant pressure on the nerve. Therefore in this paper, we present a methodology for identification of the hands including localization of the wrists, supported by a fuzzy warning system. Initially, on the images taken by a camera mounted above a laptop monitor, hands and the left wrist are recognized. Subsequently, angle of the wrist is estimated and the fuzzy warning system is triggered by the angle and the duration, as inputs. While putting forward a new monitoring protocol, a novel application area of fuzzy warning system in preventive medicine is presented to improve the health of individuals and quality of life.

Abstract - Pollution routing problem (PRP) is an NP-hard multi-objective optimization problem. The main goal is pollution reduction and secondary goals are cost/distance minimization, profit maximization etc. We have considered two unique models with two different set of objectives viz. (i) distance and fuel consumption, and (ii) weighted load and fuel consumption. Here, system parameters like demand, driver wages, timing constraints etc. can't be predicted a-priori and involve multiple opinions from the designers. Thus, such uncertain system parameters can be modelled using fuzzy sets. As type-1 fuzzy sets (T1 FSs) has limitations in modelling higher order uncertainty, this paper models these uncertain parameters with interval type-2 fuzzy sets (IT2 FSs). We have solved the problem by an efficient multi-objective evolutionary algorithm viz. NSGA-II (non- dominated sorting genetic algorithm-II). Numerical examples demonstrate the efficiency of the proposed technique over existing (crisp and type-1 fuzzy set based) approaches.