Introducing Fuzzy Cognitive Maps for developing Decision Support System for Triage at Emergency Room Admissions for the Elderly

Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application

This paper determines the risk for cardiovascular diseases (CVDs), and nutrition level in infants aged 0–6 months using Fuzzy Cognitive Maps (FCMs). The aim of this study is to facilitates the medical experts to early detects these diseases with accuracy, so that overall death ratio can be reduced. Firstly, we have introduced the concepts of FCMs and briefly refer to the applications of these methods in medical. After that, two intelligent decision support systems for cardiovascular and malnutrition are developed using FCMs. The proposed cardiovascular risk assessment system takes six inputs: chest pain, cholesterol, heart rate, blood pressure, blood sugar, and old peak and determines CVDs risk. The second decision support system of malnutrition diagnosis takes twelve inputs: breastfeeding, daily income, maternal education, colostrum intake, energy intake, protein intake, vitamin A intake, iron intake, family size, height, weight, head circumference, and skin fold thickness and diagnoses the nutrition level in infants. We have explained the working of both decision support systems using case studies.

Decision support systems in crowdfunding: A fuzzy cognitive maps (FCM) approach

This work presents a Fuzzy Cognitive Map Medical Decision Support System (FCM-MDSS) for the hospital admission procedure of elderly patients. The FCMMDSS is applied to the Emergency Department (ED), where elderly patients arrive requesting medical assistance. Here a new hybrid methodology is introduced to develop FCM-MDSS exploiting human experience and accompanied by available bibliographic information. It is based on the widely applied Triage complex decision-making process and the generally accepted procedures while trying to minimize unnecessary admissions as well as over/under-triaging. The FCM-MDSS is evaluated for known cases of real patients arriving at the ED from the literature.

Decision–support systems in the field of integrated water management could benefit considerably from social science knowledge, as many environmental changes are human-induced. Unfortunately the adequate incorporation of qualitative social science concepts in a quantitative modeling framework is not straightforward. The applicability of fuzzy set theory and fuzzy cognitive maps for the integration of qualitative scenarios in a decision–support system was examined for the urbanization of the coastal city of Ujung Pandang, Indonesia. The results indicate that both techniques are useful tools for the design of integrated models based on a combination of concepts from the natural and social sciences.

This work focuses on the formalization of a Fuzzy Cognitive Map based decision support system using complementary the fuzzy decision tree method for producing fuzzy If-Then rules (extracted from data) accompanied with the available experts' knowledge. The proposed approach is applied to build a Fuzzy Cognitive Map based decision support system (FCM- DSS) used in medical informatics. In this approach,

Diagnosis decision making in medicine needs to take into account the patient’s clinical parameters, the context of illness and the medical knowledge of the physician to discuss and confirm a diagnosis. More and more decision support systems have to integrate different types of knowledge bases including the clinical practice guidelines. Fuzzy Cognitive Maps is a soft computing technique capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. FCM are limited to the representation of simple monotonic causal relations between concepts, however Rule Based Fuzzy Cognitive Maps start from a traditional fuzzy architecture with feedback in order to overcome FCM weaknesses. We used these two methods for modelling knowledge of Clinical Practice Guidelines and we used Semantic Web tools to implement them. We identified 25 clinical concepts and 13 diagnosis concepts. We defined 320 rules to implement FCM and 623 rules for RBFCM. 92% of the diagnoses proposed by the FCM and 94% of the diagnoses proposed by the RBFCM were correct.

The challenging issues of modelling and controlling complex dynamic systems (CDS) are carefully considered and properly addressed. Qualitative description of most of the parameters of complex dynamic systems results inevitably in fuzziness, complexity and uncertainty. One of the challenges of accepting the operation of any complex dynamic system is the ability to make Decisions so the system runs efficiently and cost effectively. A short but useful historical overview of decision support systems (DSS) is given. The need for new conceptual and advanced models for addressing the challenging issues of CDS is provided. Fuzzy Cognitive Maps (FCMs) as a new modelling approach is briefly described. An illustrative example is provided along with some very interesting promising results.

Numerous urban mobility projects attempt to utilize near zero-CO2 emission practices in order to reduce greenhouse effect. Most initiatives attempt to mitigate the effects of climate change through controlling the resources consumed by human activity. In this study, the assessment of the degree by which these initiatives affect urban mobility and resulting impacts utilizes a fuzzy semi-quantitative methodology with the inclusion of all stakeholders into an innovative Decision Support System (DSS). This holistic approach effectively assesses the interaction of linguistic evaluations and fuzzy set theory to produce a Fuzzy Cognitive Map (FCM) that overcomes the ambiguity caused by a lack of quantitative evidence. The study strands include scenarios that emphasize the impact to sustainable urban mobility due to urbanization and the rise of urban actors (social, technical, transport, and economic) in influencing environmentally friendly decision-making. We concentrate in the use case of Larissa, Greece (city of the CIVITAS initiative) that started implementing the sustainable urban development plan in 2015. The suggested decision-making tool employs analytics and optimization algorithms to direct responsible authorities and decision-makers towards sustainable urban mobility of Larissa and eventually decarbonization of the urban and suburban area.

In this paper we use Fuzzy Cognitive Maps (FCMs), a well-established Artificial Intelligence technique that incorporates ideas from Artificial Neural Networks and Fuzzy Logic, to create a dynamic model of Turkey’s course towards its integration into the European Union, after the decision of December 18, 2004, according to which in October 3, 2005, Turkey will start negotiating its access to the European Union. FCMs create models as collections of concepts and the various causal relations that exist between these concepts. The decision capabilities of the FCM structure are examined and presented using a model developed based on the beliefs of a domain expert in the political situation in Turkey & European Union. The model is examined both statically using graph theory techniques and dynamically through simulations. Scenarios are introduced and predictions are made by viewing dynamically the consequences of the corresponding actions.

The aim of this paper is to present a new approach in modelling wine quality and quantity using Fuzzy Cognitive Maps trained by non linear Hebbian learning algorithm. The methodology described extracts the knowledge from the experts and exploits their experience on wine production. Two case studies with data from real vineyards were examined. The results of this study show that software tools using Fuzzy Cognitive Maps could be explored further and problems that arise during the wine production could be prevented by an efficient decision support system.

Decision making in the field of medical diagnosis involves a degree of uncertainty and a need to take into account the patient’s clinical parameters, the context of illness and the medical knowledge of the physician, to determine and confirm the diagnosis. In this study, we investigated and evaluated a model framework, for diagnostic decisions, based on a cognitive process and a Semantic Web approach. Fuzzy cognitive maps (FCM) are a cognitive process applying the main features of fuzzy logic and neural processors to situations involving imprecision and uncertain descriptions, in a similar way to intuitive human reasoning. We explored the use of this method for modeling clinical practice guidelines, using Semantic Web tools to implement these guidelines and for the formalization process. Twenty-five clinical and 13 diagnosis concepts were identified, to represent the problem of urinary tract infection diagnosis.

Dengue is a vector-borne tropical disease that is widely distributed throughout the world. The clinical management of dengue consists of diagnosis and treatment of the disease. The development of approaches to aid decision-making in this disease is necessary to reduce morbidity and mortality rates. Despite the existence of guidelines for clinical management, the diagnosis and treatment of dengue remain a challenge. To address this problem, we aimed to develop methodologies, models, and approaches to support decision-making regarding the clinical management of this infection. We developed several works to meet the proposed objectives. First, we reviewed the latest trends in dengue modeling using machine learning (ML) techniques. Then, we proposed a decision support system for dengue diagnosis using fuzzy cognitive maps (FCM). Subsequently, we developed an autonomous cycle of data analysis tasks (ACODAT) to support both diagnosis and treatment of the disease. Also, we presented a methodology based on FCM and optimization algorithms to generate prescriptive models in various science domains. Finally, we proposed three federated learning approaches to ensure the security and privacy of data related to the clinical management of dengue. These strategies were evaluated using diverse datasets with signs, symptoms, laboratory tests, and information related to disease treatment. The results showed the ability of the developed methodologies and models to predict the disease, classify patients according to severity, evaluate the behavior of severity-related variables, and recommend treatments based on World Health Organization (WHO) guidelines.

This chapter discusses individual-based models (IBMs) and uses the Overview, Design concepts, and Details (ODD) protocol to describe a predator-prey evolutionary ecosystem IBM called EcoSim. EcoSim is one of the most complex and large-scale IBMs of its kind, allowing hundreds of thousands of intricate individuals to interact and evolve over thousands of time steps. Individuals in EcoSim have a behavioral model represented by a fuzzy cognitive map (FCM). The FCM, described in this chapter, is a cognitive architecture well-suited for individuals in EcoSim due to its efficiency and the complexity of decision-making it allows. Furthermore, it can be encoded as a vector of real numbers, lending itself to being part of the genetic material passed on by individuals during reproduction. This allows for meaningful evolution of their behaviors and natural selection without predefined fitness. EcoSim has been enhanced to increase the breadth and depth of the questions it can answer. New features include: fertilization of primary producers by consumers, predator-prey combat, sexual reproduction, sex-linkage of genes, multiple modes of reproduction, size-based dominance hierarchy, and more. In addition to describing EcoSim in detail, we present data from default EcoSim runs to show potential users the types of data EcoSim generates. Furthermore, we present a brief sensitivity analysis of some variables in EcoSim, and a case study that demonstrates research that can be performed using EcoSim. In the case study, we elucidate some evolutionary and behavioral impacts on animals under two conditions: when primary production is limited, and when energy expenditure is reduced.

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.