A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications

Organizational culture and leadership style in Spanish Hospitals: Effects on knowledge management and efficiency

Hospital culture and leadership style have attracted considerable attention in research, with compelling evidence indicating their potential competitive advantages, including their crucial role in ensuring the successful implementation of knowledge management and its impact on hospital efficiency. The aim of this paper is to identify the effects of organizational culture and leadership style on knowledge management and hospital efficiency. Fuzzy cognitive maps (FCMs) are relational models that can be used to represent the opinions and knowledge of expert to infer cause-effect relationships among different concepts. The use of FCMs as a simulation tool enables the evaluation of potential scenarios based on different organizational cultures and leadership styles in hospitals. Developing an FCM for this study involved several steps. Firstly, data were collected through interviews with 21 experts in hospital management. The interviews were conducted between May and September 2023 either face-to-face or via videoconference. Once individual cognitive maps had been created, consensus among them was achieved through a multicriteria decision-making process, wherein the expert opinions were averaged. The separate cognitive maps of each expert were then integrated to produce a single FCM using the augmented FCM approach. Reflecting expert insights from the FCM, hospitals with a hierarchy culture exhibit diminished levels of knowledge creation, management, and overall hospital efficiency, whereas those with an adhocracy culture show improvements in knowledge creation, knowledge exploitation, and overall hospital efficiency in comparison to alternative ones. From the experts' FCM perspective regarding leadership style, transformational leadership achieves the highest level of knowledge management and hospital efficiency in hospitals with an adhocracy culture. Finally, this paper offers a reference for practising knowledge management and improving hospital efficiency through adhocracy culture and transformational leadership.

Fuzzy cognitive mapping in participatory research and decision making: a practice review

BACKGROUND

Fuzzy cognitive mapping (FCM) is a graphic technique to describe causal understanding in a wide range of applications. This practice review summarises the experience of a group of participatory research specialists and trainees who used FCM to include stakeholder views in addressing health challenges. From a meeting of the research group, this practice review reports 25 experiences with FCM in nine countries between 2016 and 2023.

RESULTS

The methods, challenges and adjustments focus on participatory research practice. FCM portrayed multiple sources of knowledge: stakeholder knowledge, systematic reviews of literature, and survey data. Methodological advances included techniques to contrast and combine maps from different sources using Bayesian procedures, protocols to enhance the quality of data collection, and tools to facilitate analysis. Summary graphs communicating FCM findings sacrificed detail but facilitated stakeholder discussion of the most important relationships. We used maps not as predictive models but to surface and share perspectives of how change could happen and to inform dialogue. Analysis included simple manual techniques and sophisticated computer-based solutions. A wide range of experience in initiating, drawing, analysing, and communicating the maps illustrates FCM flexibility for different contexts and skill bases.

CONCLUSIONS

A strong core procedure can contribute to more robust applications of the technique while adapting FCM for different research settings. Decision-making often involves choices between plausible interventions in a context of uncertainty and multiple possible answers to the same question. FCM offers systematic and traceable ways to document, contrast and sometimes to combine perspectives, incorporating stakeholder experience and causal models to inform decision-making. Different depths of FCM analysis open opportunities for applying the technique in skill-limited settings.

Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications

Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue.

Fuzzy cognitive maps for municipal governance improvement

This paper applies Fuzzy Cognitive Maps (FCMs) to understand the diverse behavior of municipal governments in Ecuador to find common elements that influence the well-being of citizens in the short and long term. Information gathering was conducted in two stages: in the first one, a group of 16 national experts was consulted to develop the initial FCM; in the second stage, local experts from 220 municipalities were interviewed to collect information on the general validity of initial FCMs and specific values given to concepts and relationships in their municipalities. Results show the importance of certain concepts for long-term municipal performance, such as the need for a competitive entrepreneurial sector, improving human resources in the municipality, and, particularly, having a competent mayor with leadership skills and a forward-looking vision that enables the development of municipal projects required to reach an efficient and equitable coverage of goods and services throughout the city. Through the application of genetic algorithms, the FCM was calibrated to ascertain the long-term dynamics of municipal development and the optimal values of the concepts that would optimize the attainment of the set objectives. The derived outcomes suggest the desirability of the maintenance of, in principle, unwanted structures like financial transfers from the central government and the need to exploit natural resources to attain urban development.

Artificial intelligence model for early detection of diabetes

Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decisionmaking in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.

[Influence of leadership style on knowledge management and hospital efficiency]

OBJECTIVE

To analyse the effect of leadership style on knowledge management in hospitals and hospital efficiency based on the opinion of experts in hospital management, applying fuzzy cognitive maps (FCM).

METHOD

FCM are relational models that can be used to graphically represent expert opinion and knowledge to infer cause-effect relationships between different concepts. The use of FCM as a simulation tool allows the evaluation of possible scenarios based on different leadership styles in hospitals.

RESULTS

In the resulting augmented matrix, standardized effects range from 0.02 to 0.84, with the highest value representing the strongest relationship between knowledge exploitation and hospital efficiency. From the viewpoint of experts, knowledge creation within the hospital also influences hospital efficiency. Regarding variables reflecting leadership characteristics, positive effects have been identified, though with varying intensities, between authority, benevolence, and charisma, both in terms of knowledge creation and exploitation, as well as hospital efficiency. The transformational leadership style is associated with coefficients having higher values for knowledge management and hospital efficiency.

CONCLUSIONS

Experts suggest that hospitals with authoritarian leadership styles would exhibit lower levels of knowledge creation and management, as well as lower hospital efficiency. On the other hand, they associate hospitals managed with a paternalistic leadership style with better values in both knowledge creation and exploitation, as well as hospital efficiency, compared to the authoritarian leadership style. Finally, they attribute the highest levels in aspects related to knowledge management and hospital efficiency to the transformational leadership style.

Modelling Differential Diagnosis of Febrile Diseases with Fuzzy Cognitive Map

The report of the World Health Organization (WHO) about the poor accessibility of people living in low-to-middle-income countries to medical facilities and personnel has been a concern to both professionals and nonprofessionals in healthcare. This poor accessibility has led to high morbidity and mortality rates in tropical regions, especially when such a disease presents itself with confusable symptoms that are not easily differentiable by inexperienced doctors, such as those found in febrile diseases. This prompted the development of the fuzzy cognitive map (FCM) model to serve as a decision-support tool for medical health workers in the diagnosis of febrile diseases. With 2465 datasets gathered from four states in the febrile diseases-prone regions in Nigeria with the aid of 60 medical doctors, 10 of those doctors helped in weighting and fuzzifying the symptoms, which were used to generate the FCM model. Results obtained from computations to predict diagnosis results for the 2465 patients, and those diagnosed by the physicians on the field, showed an average of 87% accuracy for the 11 febrile diseases used in the study. The number of comorbidities of diseases with varying degrees of severity for most patients in the study also covary strongly with those found by the physicians in the field.

Incorporating Fuzzy Cognitive Inference for Vaccine Hesitancy Measuring

Vaccine hesitancy plays a key role in vaccine delay and refusal, but its measurement is still a challenge due to multiple intricacies and uncertainties in factors. This paper attempts to tackle this problem through fuzzy cognitive inference techniques. Firstly, we formulate a vaccine hesitancy determinants matrix containing multi-level factors. Relations between factors are formulated through group decision-making of domain experts, which results in a fuzzy cognitive map. The subjective uncertainty of linguistic variables is expressed by fuzzy numbers. A double-weighted method is designed to integrate the distinguished decisions, in which the subjective hesitancy is considered for each decision. Next, three typical scenarios are constructed to identify key and sensitive factors under different experimental conditions. The experimental results are further discussed, which enrich the approaches of vaccine hesitancy estimation for the post-pandemic global recovery.

Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping

Suicide is the second leading cause of death among youth ages 10-19 in the USA. While suicide has long been recognized as a multifactorial issue, there is limited understanding regarding the complexities linking adverse childhood experiences (ACEs) to suicide ideation, attempt, and fatality among youth. In this paper, we develop a map of these complex linkages to provide a decision support tool regarding key issues in policymaking and intervention design, such as identifying multiple feedback loops (e.g., involving intergenerational effects) or comprehensively examining the rippling effects of an intervention. We use the methodology of systems mapping to structure the complex interrelationships of suicide and ACEs based on the perceptions of fifteen subject matter experts. Specifically, systems mapping allows us to gain insight into the feedback loops and potential emergent properties of ACEs and youth suicide. We describe our methodology and the results of fifteen one-on-one interviews, which are transformed into individual maps that are then aggregated and simplified to produce our final causal map. Our map is the largest to date on ACEs and suicide among youth, totaling 361 concepts and 946 interrelationships. Using a previously developed open-source software to navigate the map, we are able to explore how trauma may be perpetuated through familial, social, and historical concepts. In particular, we identify connections and pathways between ACEs and youth suicide that have not been identified in prior research, and which are of particular interest for youth suicide prevention efforts.

How Individuals With Low Back Pain Conceptualize Their Condition: A Collaborative Modeling Approach

Low back pain (LBP) is complex. This study aimed to use collaborative modeling to evaluate conceptual models that individuals with LBP have of their condition, and to compare these models with those of researchers/clinicians. Twenty-eight individuals with LBP were facilitated to generate mental models, using "fuzzy cognitive maps," that represented conceptualization of their own LBP and LBP "in general." "Components" (ie, causes, outcomes and treatments) related to pain, disability and quality of life were proposed, along with the weighted "Connections" between Components. Components were classified into thematic categories. Weighting of Connections were summed for each Component to judge relative importance. Individual models were aggregated into a metamodel. When considering their own condition, participants' models included 19(SD = 6) Components and 43(18) Connections with greatest weight on "Biomechanical" components. When considering LBP in general, models changed slightly. Patient models contrasted the more complex models of researchers/clinicians (25(7) Components; 77(42) Connections), with most weight on "Psychological" components. This study provides unique insight into how individuals with LBP consider their condition, which is largely biomedical and narrower than clinician/researcher perspectives. Findings highlight challenges for changing public perception of LBP, and provide a method with potential utility to understand how individuals conceptualize their condition. PERSPECTIVE: Collaborative modeling was used to understand how individuals with low back pain conceptualize their own condition, the condition in general, and compare this with models of expert researchers/clinicians. Data revealed issues in how individuals with back pain conceptualize their condition, and the method's potential utility for clinical evaluation of patients.

Hybrid spectrum management using integrated fuzzy and femtocells in cognitive domain

In Today’s pandemic situation, ‘Spectrum accessing and smart usage’ is the sacred Mantra uttered by every individual citizen in the world. Work from home for techies, online classes for students, games for kids, webinar for teaching fraternity etc., are going almost on indoor coverage without any limit in pace because of the smart spectrum coverage by the network service providers. This paper provides an add-on facility to the existing wireless infrastructure to provide a better user experience in this highly regrettable routine. In this paper, a cognitive domain unused spectrum holes are efficiently handled by (i) adaptive spectrum management technique; (ii) Fuzzy Inference System based spectrum administration and (iii) Hybrid Cognitive Femtocell approaches based on the user demand and their applications. The proposed integrated cognitive femtocell and Fuzzy-based approach reduces the indoor coverage problems and enhances the throughput of the macrocell users by allowing adaptive spectrum management based on the demand, thereby eliminating spectrum underlay and overlay problems during critical conditions. In cognitive femtocell networks, the access points are prepared and installed with Cognitive Radio which can determine spectrum dynamically by macrocells and nearby Femto Access Points. It adjusts its radiating parameters to evade the macrocells’ interferences and the neighbouring femtocells, thereby maximising the spectrum band’s overall utility.

Why Modelling the COVID-19 pandemic using Fuzzy Cognitive Maps (FCM)?

The coronavirus disease known today as COVID-19, has created tremendous chaos around the world, affecting people's lives and causing a large number of deaths. The WHO has accepted COVID-19 as a pandemic leading to a global health emergency. Global collaboration is sought in numerous quarters. Research efforts have been intensified all around the humankind. Most studies for COVID-19 are done based on statistical models which depend solely on correlation factors. The factor of causality has not been considered appropriately. The approach of Fuzzy Cognitive Maps (FCM) that is considering the causality factors is proposed, to investigate the whole spectrum of COVID-19. An FCM COVID-19 model is proposed having 10 symptoms-concepts. Early theoretical simulation studies using an FCM COVID-19 model and real data from the local hospital, have been conducted. Simulations with real patient data give excellent results. Future research directions are provided.

Fuzzy Logic Intelligent Systems and Methods in Midwifery and Obstetrics

Background:

Fuzzy logic can be used to model and manipulate imprecise and subjective knowledge imitating the human reasoning.

Objective:

The aim of this systematic review was to analyze research studies pertaining to fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics.

Methods:

A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only the papers that discussed fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics were considered in this review. Selected papers were critically evaluated as for their relevance and a contextual synthesis was conducted.

Results:

Twentynine papers were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that fuzzy logic and fuzzy intelligent systems have been successfully applied in midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc.

Conclusion:

This systematic review suggests that fuzzy logic is applicable to midwifery and obstetrics domains providing the means for developing affective intelligent systems that can assist human experts in dealing with complex diagnosis and problem solving. However, its full potential is not yet been examined, thus presenting an opportunity for further research.

Re-evaluation of the healthcare service quality criteria for the Covid-19 pandemic: Z-number fuzzy cognitive map

Hospitals as healthcare centers have faced many challenges with the Covid-19 spread, which results in a decline in the quality of health care. Because the number of patients referred to hospitals increases dramatically during the pandemic, providing high-quality services and satisfying them is more important than ever to maintain community health and create loyal customers in the future. However, health care quality standards are generally designed for normal circumstances. The SERVPERF standard, which measures customer perceptions of service quality, has also been adjusted for hospital service quality measurement. In this study, the SERVPERF standard criteria for health services are evaluated in the Covid-19 pandemic. For this purpose, by considering the causal relationships between the criteria and using Z-Number theory and Fuzzy Cognitive Maps (FCMs), the importance of these criteria in the prevalence of infectious diseases was analyzed. According to the results, hospital reliability, hospital hygiene, and completeness of the hospital with ratios 0.9559, 0.9305, and 0.9268 are respectively the most influential criteria in improving the quality of health services in the spread of infectious diseases circumstances such as the Covid-19 pandemic. A review of the literature shows that in previous studies, comprehensive research has not been done on prioritizing the criteria for measuring the quality of health services in the context of the spread of infectious diseases.

Prioritizing Construction Labor Productivity Improvement Strategies Using Fuzzy Multi-Criteria Decision Making and Fuzzy Cognitive Maps

Construction labor productivity (CLP) is affected by various interconnected factors, such as crew motivation and working conditions. Improved CLP can benefit a construction project in many ways, such as a shortened project life cycle and lowering project cost. However, budget, time, and resource restrictions force companies to select and implement only a limited number of CLP improvement strategies. Therefore, a research gap exists regarding methods for supporting the selection of CLP improvement strategies for a given project by quantifying the impact of strategies on CLP with respect to interrelationships among CLP factors. This paper proposes a decision support model that integrates fuzzy multi-criteria decision making with fuzzy cognitive maps to prioritize CLP improvement strategies based on their impact on CLP, causal relationships among CLP factors, and project characteristics. The proposed model was applied to determine CLP improvement strategies for concrete-pouring activities in building projects as an illustrative example. This study contributes to the body of knowledge by providing a systematic approach for selecting appropriate CLP improvement strategies based on interrelationships among the factors affecting CLP and the impact of such strategies on CLP. The results are expected to support construction practitioners with identifying effective improvement strategies to enhance CLP in their projects.

Real‑time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm

Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses Chimp Optimization Algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic Algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale Optimization Algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25 % and 99.11 % as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75 % and 1.01 % as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 s.

Distribution of COVID-19 cases and deaths in Europe during the first 12 peak weeks of outbreak

OBJECTIVE

The aim of the study was to identify similar WHO European countries in COVID-19 incidence and mortality rate during the first 12 peak weeks of pandemic outbreak to find out whether exact coherent parts of Europe were more affected than others, and to set relationship between age and higher COVID-19 mortality rate.

METHODS

COVID-19 cases and deaths from 28 February to 21 May 2020 of 37 WHO European countries were aggregated into 12 consecutive weeks. The fuzzy C-means clustering was performed to identify similar countries in COVID-19 incidence and mortality rate. Pearson product-moment correlation coefficient and log-log linear regression analyses were performed to set up relation between COVID-19 mortality rate and age. Mann-Whitney (Wilcoxon) test was used to explore differences between countries possessing higher mortality rate and age.

RESULTS

Based on the highest value of the coefficient of overall separation five clusters of similar countries were identified for incidence rate, mortality rate and in total. Analysis according to weeks offered trends where progress of COVID-19 incidence and mortality rate was visible. Pearson coefficient (0.69) suggested moderately strong connection between mortality rate and age, Mann-Whitney (Wilcoxon) test proved statistically significant differences between countries experiencing higher mortality rate and age vs. countries having both indicators lower (p < 0.001). Log-log linear regression analysis defined every increase in life expectancy at birth in total by 1% meant growth in mortality rate by 22% (p < 0.001).

CONCLUSION

Spain, Belgium and Ireland, closely followed by Sweden and Great Britain were identified as the worst countries in terms of incidence and mortality rate in the monitored period. Luxembourg, Belarus and Moldova accompanied the group of the worst countries in terms of incidence rate and Italy, France and the Netherland in terms of mortality rate. Correlation analysis and the Mann-Whitney (Wilcoxon) test proved statistically significant positive relationship between mortality rate and age. Log-log linear regression analysis proved that higher age accelerated the growth of mortality rate.

Radiomics Analysis to Enhance Precise Identification of Epidermal Growth Factor Receptor Mutation Based on Positron Emission Tomography Images of Lung Cancer Patients

How to recognize precisely epidermal growth factor receptor (EGFR) mutation in lung cancer patients owns great clinical requirement. In this study, 1575 radiomics features were extracted from PET images of 75 lung cancer patients based on contrast agents such as 18F-MPG and 18F-FDG. The Mann-Whitney U test was used for single factor analysis, the Least Absolute Shrinkage and Selection Operator (Lasso) Regression was used for feature screening, then the radiomics classification models were established by using support vector machines and ten-fold cross-validation, and were used to identify EGFR mutation in primary lung cancers and metastasis lung cancers, accuracy based on 18F-MPG PET images are respectively 90% for primary lung cancers, and 89.66% for metastasis lung cancers, accuracy based on 18F-FDG PET images are respectively 76% for primary lung cancers and 82.75% for metastasis lung cancers. The area under the curves (AUC) based on 18F-MPG PET images are respectively 0.94877 for primary lung cancers, and 0.91775 for metastasis lung cancers, AUC based on 18F-FDG PET images are respectively 0.87374 for primary lung cancers, and 0.82251 for metastasis lung cancers. In conclusion, both 18F-MPG PET images and 18F-FDG PET images combined with established classification models can identify EGFR mutation, but 18F-MPG PET images have more precisely than 18F-FDG PET images, own clinical translational prospects.

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study.

A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

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.

Analysis and decision based on specialist self-assessment for prognosis factors of acute leukemia integrating data-driven Bayesian network and fuzzy cognitive map

The purpose of the present study is to analyze the prognostic factors of acute leukemia and to construct a decision model based on a causal relationship between the factors of this disease to assist medical specialists. In medical decisions, to reach effective, quick, and reliable results, there is a need for a simple decision-making model based on a specialist's self-assessment. It may help the medical team before final diagnosis by costly and time-consuming procedures such as bone marrow sampling and pathological test as well as provide an appropriate prognosis and diagnosis tool. Because of the complex and not the well-defined structure of medical data, the use of intelligent methods must be considered. For this purpose, first, a data-driven Bayesian network (BN) and Greedy algorithm are employed to determine causal relationships and probability between nodes using the real set of data. Then, these causal relationships will form based on the fuzzy cognitive map (FCM). Finally, according to scenarios defined, the results are analyzed. These analyses are also repeated for each type of acute leukemia including acute lymphocytic leukemia (ALL) and acute myelocytic leukemia (AML). Graphical abstract.

Prediction of DNA-Binding Residues in Local Segments of Protein Sequences with Fuzzy Cognitive Maps

While protein-DNA interactions are crucial for a wide range of cellular functions, only a small fraction of these interactions was annotated to date. One solution to close this annotation gap is to employ computational methods that accurately predict protein-DNA interactions from widely available protein sequences. We present and empirically test first-of-its-kind predictor of DNA-binding residues in local segments of protein sequences that relies on the Fuzzy Cognitive Map (FCM) model. The FCM model uses information about putative solvent accessibility, evolutionary conservation, and relative propensities of amino acid to interact with DNA to generate putative DNA-binding residues. Empirical tests on a benchmark dataset reveal that the FCM model secures AUC = 0.72 and outperforms recently released hybridNAP predictor and several popular machine learning methods including Support Vector Machines, Naïve Bayes, and k-Nearest Neighbor. The improvements in the predictive performance result from an intrinsic feature of FCMs that incorporate relations between the input features, besides the relations between the inputs and output that are modelled by other algorithms. We also empirically demonstrate that use of a short sliding window results in further improvements in the predictive quality. The funDNApred webserver that implements the FCM predictor is available at http://biomine.cs.vcu.edu/servers/funDNApred/.

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non-linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10-fold cross-validation, for 110 real cases, and the results were compared with those of support vector machine and K-nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non-commercial purposes.

Fuzzy logic based approaches for gene regulatory network inference

The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, etc., is growing exponentially. These biological data are analyzed using various computational techniques for knowledge discovery - which is also one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays a pivotal role in understanding gene regulation processes and disease mechanism at the molecular level. From last couple of decades, researchers are interested in developing computational algorithms for GRN inference (GRNI) from high-throughput experimental data. Several computational approaches have been proposed for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression-based approaches, probabilistic approaches (Bayesian networks, naïve byes), artificial neural networks and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approaches, is a well-studied technique in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches developed during last two decades for GRNI.

Automated conflict resolution between multiple clinical pathways: a technology report

BACKGROUND

The number of people in the UK with three or more long-term conditions continues to grow and the management of patients with co-morbidities is complex. In treating patients with multimorbidities, a fundamental problem is understanding and detecting points of conflict between different guidelines which to date has relied on individual clinicians collating disparate information.

OBJECTIVE

We will develop a framework for modelling a diverse set of care pathways, and investigate how conflicts can be detected and resolved automatically. We will use this knowledge to develop a software tool for use by clinicians that can map guidelines, highlight root causes of conflict between these guidelines and suggest ways they might be resolved.

METHOD

Our work consists of three phases. First, we will accurately model clinical pathways for six of the most common chronic diseases; second, we will automatically identify and detect sources of conflict across the pathways and howthey might be resolved. Third, we will present a case study to prove the validity of our approach using a team of clinicians to detect and resolve the conflicts in the treatment of a fictional patient with multiple common morbidities and compare their findings and recommendations with those derived automatically using our novel software.

DISCUSSION

This paper describes the development of an important software-based method for identifying a conflict between clinical guidelines. Our findings will support clinicians treating patients with multimorbidity in both primary and secondary care settings.

Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review

BACKGROUND AND OBJECTIVE

Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices.

METHODS

Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.

RESULTS

Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.

CONCLUSIONS

Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.