“Medical Decision Support Systems based on Soft Computing Techniques” (original) (raw)
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Fuzzy Cognitive Maps Structure for Medical Decision Support Systems
Fuzzy Cognitive Maps (FCMs) are a soft computing technique that follows an approach similar to human reasoning and human decision-making process, considering them a valuable modeling and simulation methodology. FCMs can successfully represent knowledge and experience, introducing concepts for the essential elements and through the use of cause and effect relationships among the concepts Medical Decision Systems are complex systems consisting of irrelevant and relevant subsystems and elements, taking into consideration many factors that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall diagnosis with a different degree. Thus, FCMs are suitable to model Medical Decision Support Systems and the appropriate FCM structures are developed as well as corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.
Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy Cognitive Maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and /or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.
Medical decision making through fuzzy computational intelligent approaches
2009
Abstract. A new approach for the construction of Fuzzy Cognitive Maps augmented by knowledge through fuzzy rule-extraction methods for medical decision making is investigated. This new approach develops an augmented Fuzzy Cognitive Mapping based Decision Support System combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods.
Fuzzy cognitive map architectures for medical decision support systems
Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.
“Hybrid model based on Decision Trees and Fuzzy Cognitive Maps for Medical Decision Support System”
For medical decision making processes (diag- nosing, classification, etc.) all decisions must be made effec- tively and reliably. Conceptual decision making models with the potential of learning capabilities are more appropriate and suitable for performing such hard tasks. Decision trees are a well known technique, which has been applied in many medi- cal systems to support decisions based on a set of instances. On the other hand, the soft computing technique of Fuzzy Cogni- tive Maps (FCMs) is an effective decision making technique, which provides high performance with a conceptual represen- tation of gathered knowledge a nd existing experience. FCMs have been used for medical decision making with emphasis in radiotherapy and classification tasks for bladder tumour grad- ing. This paper proposes and presents an hybrid model de- rived from the combination and the synergistic application of the above mentioned techniques. The proposed Decision Tree- Fuzzy Cognitive Map model has enhanced operation and effec- tiveness based on both methods giving better accuracy results in medical decision tasks.
A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems
Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology, 2013
This study examines and compares different Fuzzy Cognitive Map structures that researchers have proposed for developing Medical Decision Support Systems. Fuzzy Cognitive Maps are a soft computing technique that have gained a good reputation in the last decade and have been used successfully in different medical fields for decision making, diagnosis and classification.
Use of Fuzzy Logic Based Decision Support Systems in Medicine
Studies on Ethno-Medicine, 2016
The complexity of the problems that are faced within people's decision-making process can reveal a variety of challenges in the solution process. The increasing complexity of the events faced, makes the decisionmaking more difficult. Therefore, recently, a trend has occurred in advanced technologies such as decision support systems (DSS). DSS offer alternative solutions with a flexible and objective perspective to researchers in various fields, particularly in the fields of medicine and life science. DSS can be designed using artificial intelligence based methods such as fuzzy logic (FL), and artificial neural networks (ANN). Nowadays, the fuzzy logic-based DSS in the medical field such as the disease diagnosis, the determination of appropriate treatment, the costs a nd so on, including issues in making clinical decisions are widely used, and successfully applied. In this study, FL-based DSS have been introduced, and different applications used in the medicine field have been given. The mea n of the success level of the FL-based DSS was determined to be ninety percent. FL-based DSS has been providi ng a significant contribution to disease diagnosis in the examined studies.
Applying Soft Computing to Clinical Decision Support
Applying Business Intelligence to Clinical and Healthcare Organizations
This article aims to explain the construction process of the learing systems based on Artificial Neural Networks and Genetic Algorithms. These systems were implemented using R and Python programming languages, in order to compare results and achieve the best solution and it was used Diabetes and Parkinson datasets with the purpose of identifying the carriers of these diseases.
“Fuzzy Cognitive Maps for Medical Diagnosis Support - A paradigm from Obstetrics”
Medical Decision Support Systems can provide assistance in crucial clinical judgm ents, particularly for inexperienced medical professionals. Fuzzy Cognitive Maps (FCMs) is a soft computing technique for mo d eling complex systems follow ing an approach similar to human reasoning and decision - making. FCMs successfully represent knowledge a nd human experience, introducing co n cepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical Decision Systems are complex syst ems that can be decomposed to su b systems a nd elements, where many factors have to be taken into consideration that may be complementary, contr a dictory, and competitive; these factors influence each other and determine the overall clinical decision with varying degree s . Here a Medical Decision Sup port System based on an appropriate FCM arch i tecture is proposed and developed , as well as a corresponding paradigm from obstetrics is d e scribed.