Fuzzy cognitive map architectures for medical decision support systems

Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps

Objective

Effective internet interventions often combine online self-help with regular professional guidance. In the absence of regularly scheduled contact with a professional, the internet intervention should refer users to professional human care if their condition deteriorates. The current article presents a monitoring module to recommend proactively seeking offline support in an eMental health service to aid older mourners.

Method

The module consists of two components: a user profile that collects relevant information about the user from the application, enabling the second component, a fuzzy cognitive map (FCM) decision-making algorithm that detects risk situations and to recommend the user to seek offline support, whenever advisable. In this article, we show how we configured the FCM with the help of eight clinical psychologists and we investigate the utility of the resulting decision tool using four fictitious scenarios.

Results

The current FCM algorithm succeeds in detecting unambiguous risk situations, as well as unambiguously safe situations, but it has more difficulty classifying borderline cases correctly. Based on recommendations from the participants and an analysis of the algorithm's erroneous classifications, we propose how the current FCM algorithm can be further improved.

Conclusion

The configuration of FCMs does not necessarily demand large amounts of privacy-sensitive data and their decisions are scrutable. Thus, they hold great potential for automatic decision-making algorithms in mental eHealth. Nevertheless, we conclude that there is a need for clear guidelines and best practices for developing FCMs, specifically for eMental health.

Fuzzy Cognitive Maps with Bird Swarm Intelligence Optimization-Based Remote Sensing Image Classification

Remote sensing image (RSI) scene classification has become a hot research topic due to its applicability in different domains such as object recognition, land use classification, image retrieval, and surveillance. During RSI classification process, a class label will be allocated to every scene class based on the semantic details, which is significant in real-time applications such as mineral exploration, forestry, vegetation, weather, and oceanography. Deep learning (DL) approaches, particularly the convolutional neural network (CNN), have shown enhanced outcomes on the RSI classification process owing to the significant aspect of feature learning as well as reasoning. In this aspect, this study develops fuzzy cognitive maps with a bird swarm optimization-based RSI classification (FCMBS-RSIC) model. The proposed FCMBS-RSIC technique inherits the advantages of fuzzy logic (FL) and swarms intelligence (SI) concepts. In order to transform the RSI into a compatible format, preprocessing is carried out. Besides, the features are produced by the use of the RetinaNet model. Besides, a FCM-based classifier is involved to allocate proper class labels to the RSIs and the classification performance can be improved by the design of bird swarm algorithm (BSA). The performance validation of the FCMBS-RSIC technique takes place using benchmark open access datasets, and the experimental results reported the enhanced outcomes of the FCMBS-RSIC technique over its state-of-the-art approaches.

Understanding the Cognitive Components of Coastal Risk Assessment

Nowadays, erosion and flooding risks represent a serious threat to coastal areas and this trend will be worsened due to climate change. The increasing concentration of population in coastal areas has a negative impact on the coastal ecosystem due to change in land use and the exploitation of natural resources, which has also increased exposure to coastal hazards. Risk assessment is hence a primary topic in coastal areas and are often affected by mismanagement and competition of interest between stakeholders. This paper presents an integrated model for coastal risk assessment as well as its application on a test site in the Puglia Region (Southern Italy). An innovative approach has been developed combining a traditional index-based model, exploiting a Drivers-Pressures-State-Impact-Response framework (DPSIR), with stakeholder’s and policy makers’ engagement by using the Future Workshop method and complementary individual working sessions structured through the use of Fuzzy-Cognitive Maps. The study shows that stakeholders’ and policy makers’ risk perception play a key role in coastal risk management and that the integration of physical risk with social perception is relevant to develop more effective management following the basics of Integrated Coastal Zone Management.

Fuzzy cognitive maps (FCMs) for the analysis of team communication

Communication in teams plays a vital role in team success. This work proposes Fuzzy Cognitive Maps (FCMs) as a formalized, team communication methodology for the analysis of content and flow, simultaneously, of naturalistic team communication in a structured environment. Several methods of analysis of team communication exist. Few of them, however, analyze the flow and content of communication simultaneously, and none with teams using naturalistic language. Team communication data is coded for flow and content (through speech acts), then turned into FCMs for visual and statistical analysis. Results show that when using the FCM methodology both flow and content of communication can be diagrammed at the individual level, however, when assessing at the team level, what is illustrated is the flow of content. The application of different statistical analyses to the FCM output provides the opportunity to answer various questions on team communication. This study demonstrates that by using speech acts (SAs) as nodes, FCMs can illustrate and analyze team communication flow and content for naturalistic language. The FCM methodology is a powerful tool for studying team communication, and further incremental work could advance knowledge in team communication dynamics and provide contributions to graph theory. This method provides a visual overview of team communication dynamics in a naturalistic language setting, thus allowing for the study of intra-team dynamics and inter-team dynamics simultaneously.

Fuzzy cognitive mapping: An old tool with new uses in nursing research

AIMS

Describe the implementation and uses of fuzzy cognitive mapping (FCM) as a constructive method for meeting the unique and rapidly evolving needs of nursing inquiry and practice.

DESIGN

Discussion paper.

DATA SOURCES

Drawing on published scholarship of cognitive mapping from the fields of ecological management, information technology, economics, organizational behaviour and health development, we consider how FCM can contribute to contemporary challenges and aspirations of nursing research.

IMPLICATIONS FOR NURSING

Fuzzy cognitive mapping can generate theory, describe knowledge systems in comparable terms and inform questionnaire design and dialogue. It can help build participant-researcher partnerships, elevate marginalized voices and facilitate intercultural dialogue. As a relatively culturally safe and foundational approach in participatory research, we suggest that FCM should be used in settings of transcultural nursing, patient engagement, person- and family-centred care and research with marginalized populations. FCM is amenable to rigorous analysis and simultaneously allows for greater participation of stakeholders.

CONCLUSION

In highly complex healthcare contexts, FCM can act as a common language for defining challenges and articulating solutions identified within the nursing discipline.

IMPACT

There is a need to reconcile diverse sources of knowledge to meeting the needs of nursing inquiry. FCM can generate theory, describe knowledge systems, facilitate dialogue and support questionnaire design. In its capacity to engage multiple perspectives in defining problems and identifying solutions, FCM can contribute to advancing nursing research and practice.

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

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.

Fuzzy Cognitive Map scenario-based medical decision support systems for education

Soft Computing (SC) techniques are based on exploiting human knowledge and experience and they are extremely useful to model any complex decision making procedure. Thus, they have a key role in the development of Medical Decision Support Systems (MDSS). The soft computing methodology of Fuzzy Cognitive Maps has successfully been used to represent human reasoning and to infer conclusions and decisions in a human-like way and thus, FCM-MDSSs have been developed. Such systems are able to assist in critical decision-making, support diagnosis procedures and consult medical professionals. Here a new methodology is introduced to expand the utilization of FCM-MDSS for learning and educational purposes using a scenario-based learning (SBL) approach. This is particularly important in medical education since it allows future medical professionals to safely explore extensive "what-if" scenarios in case studies and prepare for dealing with critical adverse events.

Diagnosis support system based on clinical guidelines: comparison between case-based fuzzy cognitive maps and Bayesian networks

Several studies have described the prevalence and severity of diagnostic errors. Diagnostic errors can arise from cognitive, training, educational and other issues. Examples of cognitive issues include flawed reasoning, incomplete knowledge, faulty information gathering or interpretation, and inappropriate use of decision-making heuristics. We describe a new approach, case-based fuzzy cognitive maps, for medical diagnosis and evaluate it by comparison with Bayesian belief networks. We created a semantic web framework that supports the two reasoning methods. We used database of 174 anonymous patients from several European hospitals: 80 of the patients were female and 94 male with an average age 45±16 (average±stdev). Thirty of the 80 female patients were pregnant. For each patient, signs/symptoms/observables/age/sex were taken into account by the system. We used a statistical approach to compare the two methods.

Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection

Uncomplicated urinary tract infection (uUTI) is a bacterial infection that affects individuals with normal urinary tracts from both structural and functional perspective. The appropriate antibiotics and treatment suggestions to individuals suffer of uUTI is an important and complex task that demands a special attention. How to decrease the unsafely use of antibiotics and their consumption is an important issue in medical treatment. Aiming to model medical decision making for uUTI treatment, an innovative and flexible approach called fuzzy cognitive maps (FCMs) is proposed to handle with uncertainty and missing information. The FCM is a promising technique for modeling knowledge and/or medical guidelines/treatment suggestions and reasoning with it. A software tool, namely FCM-uUTI DSS, is investigated in this work to produce a decision support module for uUTI treatment management. The software tool was tested (evaluated) in a number of 38 patient cases, showing its functionality and demonstrating that the use of the FCMs as dynamic models is reliable and good. The results have shown that the suggested FCM-uUTI tool gives a front-end decision on antibiotics' suggestion for uUTI treatment and are considered as helpful references for physicians and patients. Due to its easy graphical representation and simulation process the proposed FCM formalization could be used to make the medical knowledge widely available through computer consultation systems.

Design and application of a generic clinical decision support system for multiscale data

Medical research and clinical practice are currently being redefined by the constantly increasing amounts of multiscale patient data. New methods are needed to translate them into knowledge that is applicable in healthcare. Multiscale modeling has emerged as a way to describe systems that are the source of experimental data. Usually, a multiscale model is built by combining distinct models of several scales, integrating, e.g., genetic, molecular, structural, and neuropsychological models into a composite representation. We present a novel generic clinical decision support system, which models a patient's disease state statistically from heterogeneous multiscale data. Its goal is to aid in diagnostic work by analyzing all available patient data and highlighting the relevant information to the clinician. The system is evaluated by applying it to several medical datasets and demonstrated by implementing a novel clinical decision support tool for early prediction of Alzheimer's disease.

Proof of impact and pipeline planning: directions and challenges for social audit in the health sector

Social audits are typically observational studies, combining qualitative and quantitative uptake of evidence with consultative interpretation of results. This often falters on issues of causality because their cross-sectional design limits interpretation of time relations and separation out of other indirect associations.

Social audits drawing on methods of randomised controlled cluster trials (RCCT) allow more certainty about causality. Randomisation means that exposure occurs independently of all events that precede it – it converts potential confounders and other covariates into random differences. In 2008, CIET social audits introduced randomisation of the knowledge translation component with subsequent measurement of impact in the changes introduced. This “proof of impact” generates an additional layer of evidence in a cost-effective way, providing implementation-ready solutions for planners.

Pipeline planning is a social audit that incorporates stepped wedge RCCTs. From a listing of districts/communities as a sampling frame, individual entities (communities, towns, districts) are randomly assigned to waves of intervention. Measurement of the impact takes advantage of the delay occasioned by the reality that there are insufficient resources to implement everywhere at the same time. The impact in the first wave contrasts with the second wave, which in turn contrasts with a third wave, and so on until all have received the intervention. Provided care is taken to achieve reasonable balance in the random allocation of communities, towns or districts to the waves, the resulting analysis can be straightforward.

Where there is sufficient management interest in and commitment to evidence, pipeline planning can be integrated in the roll-out of programmes where real time information can improve the pipeline. Not all interventions can be randomly allocated, however, and random differences can still distort measurement. Other issues include contamination of the subsequent waves, ambiguity of indicators, “participant effects” that result from lack of blinding and lack of placebos, ethics and, not least important, the skills to do pipeline planning correctly.

Building the community voice into planning: 25 years of methods development in social audit

Health planners and managers make decisions based on their appreciation of causality. Social audits question the assumptions behind this and try to improve quality of available evidence. The method has its origin in the follow-up of Bhopal survivors in the 1980s, where "cluster cohorts" tracked health events over time. In social audit, a representative panel of sentinel sites are the framework to follow the impact of health programmes or reforms. The epidemiological backbone of social audit tackles causality in a calculated way, balancing computational aspects with appreciation of the limits of the science.Social audits share findings with planners at policy level, health services providers, and users in the household, where final decisions about use of public services rest. Sharing survey results with sample communities and service workers generates a second order of results through structured discussions. Aggregation of these evidence-based community-led solutions across a representative sample provides a rich substrate for decisions. This socialising of evidence for participatory action (SEPA) involves a different skill set but quality control and rigour are still important.Early social audits addressed settings without accepted sample frames, the fundamentals of reproducible questionnaires, and the logistics of data turnaround. Feedback of results to stakeholders was at CIET insistence--and at CIET expense. Later social audits included strong SEPA components. Recent and current social audits are institutionalising high level research methods in planning, incorporating randomisation and experimental designs in a rigorous approach to causality.The 25 years have provided a number of lessons. Social audit reduces the arbitrariness of planning decisions, and reduces the wastage of simply allocating resources the way they were in past years. But too much evidence easily exceeds the uptake capacity of decision takers. Political will of governments often did not match those of donors with interest conditioned by political cycles. Some reforms have a longer turnaround than the political cycle; short turnaround interventions can develop momentum. Experience and specialisation made social audit seem more simple than it is. The core of social audit, its mystique, is not easily taught or transferred. Yet teams in Mexico, Nicaragua, Canada, southern Africa, and Pakistan all have more than a decade of experience in social audit, their in-service training supported by a customised Masters programme.

Fuzzy cognitive maps for medical decision support - a paradigm from obstetrics

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 following an approach similar to human reasoning and decision-making. FCMs 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 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 varying degrees. Here a Medical Decision Support System based on an appropriate FCM architecture is proposed and developed, as well as a corresponding paradigm from obstetrics is described.