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Apostolopoulos ID, Papandrianos NI, Papathanasiou ND, Papageorgiou EI. Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study. Bioengineering (Basel) 2024; 11:139. [PMID: 38391626 PMCID: PMC10886348 DOI: 10.3390/bioengineering11020139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/18/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024] Open
Abstract
Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.
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Affiliation(s)
| | - Nikolaos I Papandrianos
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
| | | | - Elpiniki I Papageorgiou
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
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Mkhitaryan S, Giabbanelli PJ, Wozniak MK, de Vries NK, Oenema A, Crutzen R. How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake. BMC Public Health 2023; 23:2478. [PMID: 38082297 PMCID: PMC10714655 DOI: 10.1186/s12889-023-17367-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. OBJECTIVE Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. METHODS A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. RESULTS Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. CONCLUSIONS Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness.
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Affiliation(s)
- Samvel Mkhitaryan
- Department of Health Promotion, CAPHRI, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
| | - Philippe J Giabbanelli
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH, USA
| | - Maciej K Wozniak
- KTH Royal Institute of Technology: Stockholm, Stockholm, SE, Sweden
| | - Nanne K de Vries
- Department of Health Promotion, CAPHRI, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - Anke Oenema
- Department of Health Promotion, CAPHRI, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - Rik Crutzen
- Department of Health Promotion, CAPHRI, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
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Schuerkamp R, Liang L, Rice KL, Giabbanelli PJ. Simulation Models for Suicide Prevention: A Survey of the State-of-the-Art. COMPUTERS (BASEL, SWITZERLAND) 2023; 12:10.3390/computers12070132. [PMID: 37869477 PMCID: PMC10588059 DOI: 10.3390/computers12070132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions.
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Affiliation(s)
- Ryan Schuerkamp
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
| | - Luke Liang
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
| | - Ketra L. Rice
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention (CDC), Atlanta, GA 30341, USA
| | - Philippe J. Giabbanelli
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
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Abbaspour Onari M, Jahangoshai Rezaee M. Implementing bargaining game-based fuzzy cognitive map and mixed-motive games for group decisions in the healthcare supplier selection. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10432-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
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5
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Chhabra M, Sharan B, Kumar M. A fuzzy cognitive map of the quality of user experience determinants in mobile application design. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-222111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The users of mobile phone are exponentially increasing. The applications are developed every day in a variety of domains to enhance the Quality of User Experience (QoUE) along with utility determinants. The design of the mobile application impacts the QoUE. QoUE in mobile applications is a measure that describes the appropriateness of the purpose of the application and the need for user retention. However, the challenge is to identify, understand, focus and interconnect the variety of determinants influencing the QoUE based on mobile application design. These determinants are based on the diversity of users and the related functional needs, user-specific needs, and background functioning of the application. The modelling and analysis help mobile application developers to improve, increase and retain user engagement on the app based on improved QoUE. To do so, a qualitative analytical method is employed in the following steps. The first ever Fuzzy Cognitive Map (FCM) is proposed to show the causal-effect links of the interdependent determinants in mobile applications based on QoUE. In our model, the existence of relationships between determinants relies on a thorough literature review. The weight of these links is estimated by users of different ages and lines of work. This is performed by an empirical study based on a questionnaire filled by experts. The questionnaire is based on the formal utility and perceived QoUE-based topics. Finally, scenario-based analysis on formed FCM based on these inputs is performed. We show that small changes in cases using different direct determinants can be used to enhance QoUE. These changes can be studied before launching an application for the user, thereby limiting the need to rework the improvements based on QoUE and providing useful guidance for the possible increase in user base and behaviour change.
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Affiliation(s)
- Megha Chhabra
- Department of Computer Science & Engineering, School of Engineering, Technology Sharda University, Gr. Noida, UP, India
| | - Bhagwati Sharan
- Department of Computer Science and Engineering, APEX institute of Technology, Chandigarh University, Mohali, India
| | - Manoj Kumar
- Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE
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6
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Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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7
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Mkhitaryan S, Giabbanelli P, Wozniak MK, Nápoles G, De Vries N, Crutzen R. FCMpy: a python module for constructing and analyzing fuzzy cognitive maps. PeerJ Comput Sci 2022; 8:e1078. [PMID: 36262149 PMCID: PMC9575875 DOI: 10.7717/peerj-cs.1078] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 08/08/2022] [Indexed: 06/16/2023]
Abstract
FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge.
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Affiliation(s)
| | - Philippe Giabbanelli
- Computer Science & Software Engineering, Miami University of Ohio, Oxford, Ohio, United States
| | - Maciej K Wozniak
- Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Gonzalo Nápoles
- Cognitive Sciences and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
| | - Nanne De Vries
- Health Promotion, Maastricht University, Maastricht, Netherlands
| | - Rik Crutzen
- Health Promotion, Maastricht University, Maastricht, Netherlands
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8
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Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping. SOCIAL NETWORK ANALYSIS AND MINING 2022; 12:1-21. [DOI: 10.1007/s13278-022-00886-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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9
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Allam A, Feuerriegel S, Rebhan M, Krauthammer M. Analyzing Patient Trajectories With Artificial Intelligence. J Med Internet Res 2021; 23:e29812. [PMID: 34870606 PMCID: PMC8686456 DOI: 10.2196/29812] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 07/26/2021] [Accepted: 10/29/2021] [Indexed: 01/16/2023] Open
Abstract
In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.
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Affiliation(s)
- Ahmed Allam
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Biomedical Informatics, University Hospital of Zurich, Zurich, Switzerland
| | - Stefan Feuerriegel
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- ETH Artificial Intelligence Center, ETH Zurich, Zurich, Switzerland
- Ludwig Maximilian University of Munich, Munich, Germany
| | - Michael Rebhan
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Michael Krauthammer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Biomedical Informatics, University Hospital of Zurich, Zurich, Switzerland
- Yale Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, United States
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10
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Apostolopoulos ID, Groumpos PP, Apostolopoulos DJ. Advanced fuzzy cognitive maps: state-space and rule-based methodology for coronary artery disease detection. Biomed Phys Eng Express 2021; 7. [PMID: 33930876 DOI: 10.1088/2057-1976/abfd83] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/30/2021] [Indexed: 11/11/2022]
Abstract
According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise diagnosis of Coronary Artery Disease (CAD) is proposed. The methodology is based on State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of the traditional Fuzzy Cognitive Maps. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the proposed system and the interpretability of the decision mechanism. The proposed method is evaluated utilizing a CAD dataset from the Department of Nuclear Medicine of the University Hospital of Patras, in Greece. Several experiments are conducted to define the optimal parameters of the proposed AFCM. Furthermore, the proposed AFCM is compared with the traditional FCM approach and the literature. The experiments highlight the effectiveness of the AFCM approach, obtaining 85.47% accuracy in CAD diagnosis, showing an improvement of +7% over the traditional approach. It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease.
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Affiliation(s)
- Ioannis D Apostolopoulos
- University of Patras, Medical School, Department of Medical Physics, Rio, Achaia, PC 26504, Greece
| | - Peter P Groumpos
- University of Patras, Department Electrical and Computer Engineering, Rio, Achaia, PC 26504, Greece
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11
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Abbaspour Onari M, Yousefi S, Rabieepour M, Alizadeh A, Jahangoshai Rezaee M. A medical decision support system for predicting the severity level of COVID-19. COMPLEX INTELL SYST 2021; 7:2037-2051. [PMID: 34777959 PMCID: PMC7930528 DOI: 10.1007/s40747-021-00312-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 02/22/2021] [Indexed: 12/31/2022]
Abstract
The main assay tool of COVID-19, as a pandemic, still has significant faults. To ameliorate the current situation, all facilities and tools in this realm should be implemented to encounter this epidemic. The current study has endeavored to propose a self-assessment decision support system (DSS) for distinguishing the severity of the COVID-19 between confirmed cases to optimize the patient care process. For this purpose, a DSS has been developed by the combination of the data-driven Bayesian network (BN) and the Fuzzy Cognitive Map (FCM). First, all of the data are utilized to extract the evidence-based paired (EBP) relationships between symptoms and symptoms' impact probability. Then, the results are evaluated in both independent and combined scenarios. After categorizing data in the triple severity levels by self-organizing map, the EBP relationships between symptoms are extracted by BN, and their significance is achieved and ranked by FCM. The results show that the most common symptoms necessarily do not have the key role in distinguishing the severity of the COVID-19, and extracting the EBP relationships could have better insight into the severity of the disease.
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Affiliation(s)
| | - Samuel Yousefi
- Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
| | - Masome Rabieepour
- Pulmonary Department, Urmia University of Medical Sciences, Urmia, Iran
| | - Azra Alizadeh
- Department of Internal Medicine, Urmia University of Medical Sciences, Urmia, Iran
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12
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Poomagal S, Sujatha R, Kumar PS, Vo DVN. A fuzzy cognitive map approach to predict the hazardous effects of malathion to environment (air, water and soil). CHEMOSPHERE 2021; 263:127926. [PMID: 32822932 DOI: 10.1016/j.chemosphere.2020.127926] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 07/31/2020] [Accepted: 08/04/2020] [Indexed: 06/11/2023]
Abstract
Malathion is an organophosphorus insecticide and pesticide commonly used in crops and residential applications. The negative effects of Malathion on human health and ecosystems are of great concern. In this work, a mathematical model pivot on Fuzzy Cognitive Map (FCM) is used to analyse the causes and hazardous effects of Malathion to the environmental components (air, water and soil). Based on expert's opinion the possible factors that cause damage to health and ecosystems due to Malathion is identified, which serve as the input to the FCM. The FCM mathematically establishes the causal relation between these factors. The mathematical simulation is done by Python Programming. This approach can be used to study the interdependencies between the adverse effects of any pesticide in human health and environment due to prolonged exposure.
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Affiliation(s)
- S Poomagal
- Department of Mathematics, Anna University Chennai: University College of Engineering Kanchipuram, Kanchipuram, India.
| | - R Sujatha
- Department of Mathematics, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India; SSN-Centre for Radiation, Environmental Science and Technology (SSN-CREST), Sri Sivasubramaniya Nadar College of Engineering, Chennai, 603110, India.
| | - P Senthil Kumar
- SSN-Centre for Radiation, Environmental Science and Technology (SSN-CREST), Sri Sivasubramaniya Nadar College of Engineering, Chennai, 603110, India; Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
| | - Dai-Viet N Vo
- Center of Excellence for Green Energy and Environmental Nanomaterials (CE@GrEEN), Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam.
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13
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A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:1016284. [PMID: 33082836 PMCID: PMC7556058 DOI: 10.1155/2020/1016284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/19/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022]
Abstract
Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.
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Kocabey Çiftçi P, Unutmaz Durmuşoğlu ZD. A multi-stage learning-based fuzzy cognitive maps for tobacco use. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04860-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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15
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Performance risk assessment in public–private partnership projects based on adaptive fuzzy cognitive map. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106413] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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Dogu E, Albayrak YE, Tuncay E. Multidrug-resistant tuberculosis risk factors assessment with intuitionistic fuzzy cognitive maps. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Elif Dogu
- Industrial Engineering Department, Galatasaray University, Besiktas, Istanbul, Turkey
| | - Y. Esra Albayrak
- Industrial Engineering Department, Galatasaray University, Besiktas, Istanbul, Turkey
| | - Esin Tuncay
- Yedikule Chest Diseases & Thoracic Surgery Training & Research Hospital, Zeytinburnu, Istanbul, Turkey
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Mehryar S, Sliuzas R, Schwarz N, Sharifi A, van Maarseveen M. From individual Fuzzy Cognitive Maps to Agent Based Models: Modeling multi-factorial and multi-stakeholder decision-making for water scarcity. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 250:109482. [PMID: 31494410 DOI: 10.1016/j.jenvman.2019.109482] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/13/2019] [Accepted: 08/26/2019] [Indexed: 06/10/2023]
Abstract
Policy making for complex Social-Ecological Systems (SESs) is a multi-factorial and multi-stakeholder decision making process. Therefore, proper policy simulation in a SES should consider both the complex behavior of the system and the multi-stakeholders' interventions into the system, which requires integrated methodological approaches. In this study, we simulate impacts of policy options on a farming community facing water scarcity in Rafsanjan, Iran, using an integrated modeling methodology combining an Agent Based Model (ABM) with Fuzzy Cognitive Mapping (FCM). First, the behavioral rules of farmers and the causal relations among environmental variables are captured with FCMs that are developed with both qualitative and quantitative data, i.e. farmers' knowledge and empirical data from studies. Then, an ABM is developed to model decisions and actions of farmers and simulate their impacts on overall groundwater use and emigration of farmers in this case study. Finally, the impacts of different policy options are simulated and compared with a baseline scenario. The results suggest that a policy of facilitating farmers' participation in management and control of their groundwater use leads to the highest reduction of groundwater use and would help to secure farmers' activities in Rafsanjan. Our approach covers four main aspects that are crucial for policy simulation in SESs: 1) causal relationships, 2) feedback mechanisms, 3) social-spatial heterogeneity and 4) temporal dynamics. This approach is particularly useful for ex-ante policy options analysis.
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Affiliation(s)
- Sara Mehryar
- ITC-Faculty of Geo-Information Science & Earth Observation, University of Twente, P.O. Box 217, 7500AE, Enschede, the Netherlands; Grantham Research Institute on Climate Change and the Environment, London School of Economics and Social Science, Houghton Street, London, WC2A 2AE, United Kingdom.
| | - Richard Sliuzas
- ITC-Faculty of Geo-Information Science & Earth Observation, University of Twente, P.O. Box 217, 7500AE, Enschede, the Netherlands
| | - Nina Schwarz
- ITC-Faculty of Geo-Information Science & Earth Observation, University of Twente, P.O. Box 217, 7500AE, Enschede, the Netherlands
| | - Ali Sharifi
- ITC-Faculty of Geo-Information Science & Earth Observation, University of Twente, P.O. Box 217, 7500AE, Enschede, the Netherlands
| | - Martin van Maarseveen
- ITC-Faculty of Geo-Information Science & Earth Observation, University of Twente, P.O. Box 217, 7500AE, Enschede, the Netherlands
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18
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Pillutla VS, Giabbanelli PJ. Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.12.020] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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20
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Amirkhani A, Papageorgiou EI, Mohseni A, Mosavi MR. A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 142:129-145. [PMID: 28325441 DOI: 10.1016/j.cmpb.2017.02.021] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 02/11/2017] [Accepted: 02/17/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures. METHODS This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment. RESULTS In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category. CONCLUSIONS Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences.
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Affiliation(s)
- Abdollah Amirkhani
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Elpiniki I Papageorgiou
- Dept. of Computer Engineering, Technological Educational Institute of Central Greece, Lamia 35100, Greece.
| | - Akram Mohseni
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Mohammad R Mosavi
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
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Huang YP, Lai SL, Sandnes FE. A repeating pattern based Query-by-Humming fuzzy system for polyphonic melody retrieval. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.04.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Subramanian J, Karmegam A, Papageorgiou E, Papandrianos N, Vasukie A. An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 118:280-297. [PMID: 25697987 DOI: 10.1016/j.cmpb.2015.01.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Revised: 12/09/2014] [Accepted: 01/03/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND There is a growing demand for women to be classified into different risk groups of developing breast cancer (BC). The focus of the reported work is on the development of an integrated risk prediction model using a two-level fuzzy cognitive map (FCM) model. The proposed model combines the results of the initial screening mammogram of the given woman with her demographic risk factors to predict the post-screening risk of developing BC. METHODS The level-1 FCM models the demographic risk profile. A nonlinear Hebbian learning algorithm is used to train this model and thus to help on predicting the BC risk grade based on demographic risk factors identified by domain experts. The risk grades estimated by the proposed model are validated using two standard BC risk assessment models viz. Gail and Tyrer-Cuzick. The level-2 FCM models the features of the screening mammogram concerning normal, benign and malignant cases. The data driven Hebbian learning algorithm (DDNHL) is used to train this model in order to predict the BC risk grade based on these mammographic image features. An overall risk grade is calculated by combining the outcomes of these two FCMs. RESULTS The main limitation of the Gail model of underestimating the risk level of women with strong family history is overcome by the proposed model. IBIS is a hard computing tool based on the Tyrer-Cuzick model that is comprehensive enough in covering a wide range of demographic risk factors including family history, but it generates results in terms of numeric risk score based on predefined formulae. Thus the outcome is difficult to interpret by naive users. Besides these models are based only on the demographic details and do not take into account the findings of the screening mammogram. The proposed integrated model overcomes the above described limitations of the existing models and predicts the risk level in terms of qualitative grades. The predictions of the proposed NHL-FCM model comply with the Tyrer-Cuzick model for 36 out of 40 patient cases. With respect to tumor grading, the overall classification accuracy of DDNHL-FCM using 70 real mammogram screening images is 94.3%. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines. CONCLUSION In the perspective of clinical oncologists, this is a comprehensive front-end medical decision support system that assists them in efficiently assessing the expected post-screening BC risk level of the given individual and hence prescribing individualized preventive interventions and more intensive surveillance for high risk women.
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Affiliation(s)
- Jayashree Subramanian
- Computer Science Engineering, RVS College of Engineering and Technology, Coimbatore, India.
| | - Akila Karmegam
- Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore, India.
| | - Elpiniki Papageorgiou
- Computer Engineering Department, Technological Educational Institute of Central Greece, 3rd KM Old National Road Lamia-Athens, 35100 Lamia, Greece.
| | | | - A Vasukie
- Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore, India.
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Giabbanelli PJ, Crutzen R. Creating groups with similar expected behavioural response in randomized controlled trials: a fuzzy cognitive map approach. BMC Med Res Methodol 2014; 14:130. [PMID: 25495712 PMCID: PMC4292828 DOI: 10.1186/1471-2288-14-130] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Accepted: 12/08/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Controlling bias is key to successful randomized controlled trials for behaviour change. Bias can be generated at multiple points during a study, for example, when participants are allocated to different groups. Several methods of allocations exist to randomly distribute participants over the groups such that their prognostic factors (e.g., socio-demographic variables) are similar, in an effort to keep participants' outcomes comparable at baseline. Since it is challenging to create such groups when all prognostic factors are taken together, these factors are often balanced in isolation or only the ones deemed most relevant are balanced. However, the complex interactions among prognostic factors may lead to a poor estimate of behaviour, causing unbalanced groups at baseline, which may introduce accidental bias. METHODS We present a novel computational approach for allocating participants to different groups. Our approach automatically uses participants' experiences to model (the interactions among) their prognostic factors and infer how their behaviour is expected to change under a given intervention. Participants are then allocated based on their inferred behaviour rather than on selected prognostic factors. RESULTS In order to assess the potential of our approach, we collected two datasets regarding the behaviour of participants (n = 430 and n = 187). The potential of the approach on larger sample sizes was examined using synthetic data. All three datasets highlighted that our approach could lead to groups with similar expected behavioural changes. CONCLUSIONS The computational approach proposed here can complement existing statistical approaches when behaviours involve numerous complex relationships, and quantitative data is not readily available to model these relationships. The software implementing our approach and commonly used alternatives is provided at no charge to assist practitioners in the design of their own studies and to compare participants' allocations.
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Affiliation(s)
- Philippe J Giabbanelli
- />Interdisciplinary Research in the Mathematical and Computational Sciences (IRMACS) Centre, Simon Fraser University, Burnaby, Canada
- />UKCRC Centre for Diet and Activity Research, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ UK
| | - Rik Crutzen
- />Department of Health Promotion, Maastricht University/CAPHRI, Maastricht, The Netherlands
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Giabbanelli PJ, Crutzen R. Supporting self-management of obesity using a novel game architecture. Health Informatics J 2014; 21:223-36. [PMID: 24557604 DOI: 10.1177/1460458214521051] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Obesity has commonly been addressed using a 'one size fits all' approach centred on a combination of diet and exercise. This has not succeeded in halting the obesity epidemic, as two-thirds of American adults are now obese or overweight. Practitioners are increasingly highlighting that one's weight is shaped by myriad factors, suggesting that interventions should be tailored to the specific needs of individuals. Health games have potential to provide such tailored approach. However, they currently tend to focus on communicating and/or reinforcing knowledge, in order to suscitate learning in the participants. We argue that it would be equally, if not more valuable, that games learn from participants using recommender systems. This would allow treatments to be comprehensive, as games can deduce from the participants' behaviour which factors seem to be most relevant to his or her weight and focus on them. We introduce a novel game architecture and discuss its implications on facilitating the self-management of obesity.
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Mago VK, Morden HK, Fritz C, Wu T, Namazi S, Geranmayeh P, Chattopadhyay R, Dabbaghian V. Analyzing the impact of social factors on homelessness: a fuzzy cognitive map approach. BMC Med Inform Decis Mak 2013; 13:94. [PMID: 23971944 PMCID: PMC3766254 DOI: 10.1186/1472-6947-13-94] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Accepted: 08/12/2013] [Indexed: 11/30/2022] Open
Abstract
Background The forces which affect homelessness are complex and often interactive in nature. Social forces such as addictions, family breakdown, and mental illness are compounded by structural forces such as lack of available low-cost housing, poor economic conditions, and insufficient mental health services. Together these factors impact levels of homelessness through their dynamic relations. Historic models, which are static in nature, have only been marginally successful in capturing these relationships. Methods Fuzzy Logic (FL) and fuzzy cognitive maps (FCMs) are particularly suited to the modeling of complex social problems, such as homelessness, due to their inherent ability to model intricate, interactive systems often described in vague conceptual terms and then organize them into a specific, concrete form (i.e., the FCM) which can be readily understood by social scientists and others. Using FL we converted information, taken from recently published, peer reviewed articles, for a select group of factors related to homelessness and then calculated the strength of influence (weights) for pairs of factors. We then used these weighted relationships in a FCM to test the effects of increasing or decreasing individual or groups of factors. Results of these trials were explainable according to current empirical knowledge related to homelessness. Results Prior graphic maps of homelessness have been of limited use due to the dynamic nature of the concepts related to homelessness. The FCM technique captures greater degrees of dynamism and complexity than static models, allowing relevant concepts to be manipulated and interacted. This, in turn, allows for a much more realistic picture of homelessness. Through network analysis of the FCM we determined that Education exerts the greatest force in the model and hence impacts the dynamism and complexity of a social problem such as homelessness. Conclusions The FCM built to model the complex social system of homelessness reasonably represented reality for the sample scenarios created. This confirmed that the model worked and that a search of peer reviewed, academic literature is a reasonable foundation upon which to build the model. Further, it was determined that the direction and strengths of relationships between concepts included in this map are a reasonable approximation of their action in reality. However, dynamic models are not without their limitations and must be acknowledged as inherently exploratory.
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Affiliation(s)
- Vijay K Mago
- The Modelling of Complex Social Systems (MoCSSy) Program, The IRMACS Centre, Simon Fraser University, Burnaby, Canada.
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