Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support

This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models’ performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics’ suggestion for UTI.     

A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease

This paper presents a new method based on fuzzy cognitive map (FCM) and possibilistic fuzzy c-means (PFCM) clustering algorithm for categorizing celiac disease (CD). CD is a complex disorder whose development is affected by genetics (HLA alleles) and gluten ingestion. The celiac patients who are not treated are at a high risk of cancer, malignant lymphoma, and small bowel neoplasia. Therefore, CD diagnosis and grading are of paramount importance. The proposed FCM models human thinking for the purpose of classifying patients suffering from CD. We used the latest grading method where three grades A, B1, and B2 are used. To improve FCM efficiency and classification capability, a nonlinear Hebbian learning algorithm is applied for adjusting the FCM weights. To this end, 89 cases are studied. Three experts extracted seven main determinant characteristics of CD which were considered as FCM concepts. The mutual effects of these concepts on one another and on the final concept were expressed in the form of fuzzy rules and linguistic variables. Using the center of gravity defuzzifier, we obtained the numerical values of these weights and obtained the total weight matrix. Ultimately, combining the FCM model with PFCM algorithm, we obtained the grades A, B1, and B2 accuracies as 88, 90, and 91%, respectively. The main advantage of the proposed FCM is the good transparency and interpretability in the decision-making procedure, which make it a suitable tool for daily usage in the clinical practice.

     

Nanocomposites of poly(3‐hydroxybutyrate)/organomodified montmorillonite: Effect of the nanofiller on the polymer's biodegradation

Poly(3‐hydroxybutyrate) (PHB) is a biopolymer that can be degraded by extracellular PHB depolymerase. This enzyme is secreted by various microorganisms, but bacterial PHB depolymerases are the most widely studied. The biodegradability rate depends on various factors. By controlling them, the biodegradability rate can change and be customized, and thus, the applications of the polymer can increase and become more diverse. In this work, the role of organomodified montmorillonite (OMMT) on PHB biodegradation was investigated. Using the melt‐mixing method, nanocomposites of PHB and OMMT as the nanofiller were prepared. The enzyme was isolated from the fungus Penicillium pinophilum and the enzymatic degradation was studied for both pure polymer and its nanocomposites. It was found that, after 25 days of enzymatic degradation, the mass loss was very low, while the polymer's average molecular weight as measured by gel permeation chromatography was significantly reduced (more than 50%). Additional peaks corresponding to PHB oligomers (from pentamers to nonamers) appeared after biodegradation. This behavior was observed for pure PHB and the hybrid materials. Scanning electron microscopy imaging of the biodegraded surfaces and analysis of these images showed that the higher amount of nanoclay (10 wt %) resulted in larger biodegraded area of the specimens. The results presented here demonstrate that the presence of the nanoclays enhances the biodegradation rate of pure PHB polymer and provide quantitative data for the biodegradation of PHB/organoclay hybrid materials. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41656.     

Application of evolutionary fuzzy cognitive maps to the long-term prediction of prostate cancer

The prediction of multivariate time series is one of the targeted applications of evolutionary fuzzy cognitive maps (FCM). The objective of the research presented in this paper was to construct the FCM model of prostate cancer using real clinical data and then to apply this model to the prediction of patient's health state. Due to the requirements of the problem state, an improved evolutionary approach for learning of FCM model was proposed. The focus point of the new method was to improve the effectiveness of long-term prediction. The evolutionary approach was verified experimentally using real clinical data acquired during a period of two years. A preliminary pilot-evaluation study with 40 men patient cases suffering with prostate cancer was accomplished. The in-sample and out-of-sample prediction errors were calculated and their decreased values showed the justification of the proposed approach for the cases of long-term prediction. The obtained results were approved by physicians emerging the functionality of the proposed methodology in medical decision making.     

Fuzzy cognitive maps and cellular automata: An evolutionary approach for social systems modelling

One of the first decisions to be made when modelling a phenomenon is that of scale: at which level is the phenomenon most appropriately modelled? For some phenomena the answer may seem too obvious to warrant even asking the question, but other phenomena cover the gamut, from high to low levels of abstraction. This paper explores how two modelling approaches that are ‘at home’ at opposite ends of the abstraction spectrum can be combined to yield an evolutionary modelling approach that is especially apt for phenomena that cover a wide range in this spectrum.We employ fuzzy cognitive maps (FCMs) to model the interplay between high-level concepts, and cellular automata (CA) to model the low-level interactions between individual actors. The combination of these models carries both beyond their respective limitations: the FCM concept is extended beyond the derivation of equilibrium outcomes from static initial conditions, to time-evolving systems where conditions may vary; CA are extended beyond the emergence of patterns from local interactions, to systems where global patterns have local repercussions.The applicability of the methodology is demonstrated by modelling the spread of human immunodeficiency virus (HIV) in an environment in which injection drug users share paraphernalia.     

A weight adaptation method for fuzzy cognitive map learning

Fuzzy cognitive maps (FCMs) constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the initial experts beliefs, the recalculation of the weights corresponding to each concept every time a new strategy is adopted and the potential convergence to undesired equilibrium states. In order to update the initial knowledge of human experts and to combine the human experts structural knowledge with the training from data, a learning methodology for FCMs is proposed. This learning method, based on nonlinear Hebbian-type learning algorithm, is used to adapt the cause–effect relationships of the FCM model improving the efficiency and robustness of FCMs. A process control problem is presented and its process is investigated using the proposed weight adaptation technique.     

Application of fuzzy cognitive maps for cotton yield management in precision farming

The management of cotton yield behavior in agricultural areas is a very important task because it influences and specifies the cotton yield production. An efficient knowledge-based approach utilizing the method of fuzzy cognitive maps (FCMs) for characterizing cotton yield behavior is presented in this research work. FCM is a modelling approach based on exploiting knowledge and experience. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps to handle experts’ knowledge and on the unsupervised learning algorithm for FCMs to assess measurement data and update initial knowledge.The advent of precision farming generates data which, because of their type and complexity, are not efficiently analyzed by traditional methods. The FCM technique has been proved from the literature efficient and flexible to handle experts’ knowledge and through the appropriate learning algorithms can update the initial knowledge. The FCM model developed consists of nodes linked by directed edges, where the nodes represent the main factors in cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause–effect (weighted) relationships between the soil properties and cotton field.The proposed method was evaluated for 360 cases measured for three subsequent years (2001, 2003 and 2006) in a 5 ha experimental cotton yield. The proposed FCM model enhanced by the unsupervised nonlinear Hebbian learning algorithm, was achieved a success of 75.55%, 68.86% and 71.32%, respectively for the years referred, in estimating/predicting the yield between two possible categories (“low” and “high”). The main advantage of this approach is the sufficient interpretability and transparency of the proposed FCM model, which make it a convenient consulting tool in describing cotton yield behavior.     

Revealing market adaptation to a low carbon transport economy: Tales of hydrogen futures as perceived by fuzzy cognitive mapping

In-depth understanding of target user groups' preferences can inform the design of effective public policies for hydrogen in a competitive mobility market. Our paper attempts a re-examination of the issue based on two novelties: First, fuzzy cognitive mapping, a soft computing technique for analysing complex decision making problems, is for the first time applied in this field to elicit human cognitive structures. Secondly, hydrogen market segmentation is studied by clustering involved agents in: lay people (‘demand’), automobile salesmen (‘supply’) and experts.     

Application of evolutionary fuzzy cognitive maps for prediction of pulmonary infections

In this paper, a new evolutionary-based fuzzy cognitive map (FCM) methodology is proposed to cope with the forecasting of the patient states in the case of pulmonary infections. The goal of the research was to improve the efficiency of the prediction. This was succeeded with a new data fuzzification procedure for observables and optimization of gain of transformation function using the evolutionary learning for the construction of FCM model. The approach proposed in this paper was validated using real patient data from internal care unit. The results emerged had less prediction errors for the examined data records than those produced by the conventional genetic-based algorithmic approaches.