Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

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.

Analysis of Casual Relationships between Social Determinants of Health in Iran: Using Fuzzy Cognitive Map

BACKGROUND

Health is a fundamental issue in recent years, highlighting the importance of harmonizing the policies of any sector with health strategies. The present study aims to identify the socio-economic factors affecting health and to provide a cognitive map of the social determinants of health in Iran.

STUDY DESIGN

A retrospective cohort study.

METHODS

This study follows a developmental process with an exploratory sequential mixed methods approach. First, a meta-synthesis qualitative method determines the most critical health determinants by reviewing 54 studies from 2000 to 2019. Then, the fuzzy cognitive map (FCM) is drawn based on interviews with six experts to derive the causal relationships among the social determinants of population health in Iran.

RESULTS

Stage 1 introduces 170 factors as the health determinants, classified into four levels: individual, local, national, and global levels, with 7, 4, 13, and 3 branches, respectively. According to the causal relationships and the out-degree (od) index, the most effective factors are the economic system (18.24), governance and policy-making (17.13), and national policies (16.93). According to the degree of centrality, these factors are the economic system (33.27), health system (30.37), and governance and policy-making (30.15).

CONCLUSION

Considering health as a complex and comprehensive system, the resulting FCM displays that the policies developed in other sectors than health are profoundly affecting population's health in Iran. Specifically, the comparative analysis of this research shows that policies regarding the economic system and people's livelihood are more effective than the policies regarding the health system itself on the population's health in Iran.

An Interoperable Electronic Health Record System for Clinical Cardiology

Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials.

Multi-Device Nutrition Control

Precision nutrition is a popular eHealth topic among several groups, such as athletes, people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. Each person then follows the food plan by preparing meals and logging all food and water intake. However, the discipline demanded to follow food plans and log food intake results in high dropout rates. This article presents the concepts, requirements, and architecture of a solution that assists the nutritionist in building up and revising food plans and the user following them. It does so by minimizing human–computer interaction by integrating the nutritionist and user systems and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, smartphones, and smart bottles. An interaction time analysis using the keystroke-level model provides a baseline for comparison in future work addressing both the use of machine learning and IoT devices to reduce the interaction effort of users.

Towards a Hybrid Machine Learning Model for Intelligent Cyber Threat Identification in Smart City Environments

The concept of a smart city requires the integration of information and communication technologies and devices over a network for the better provision of services to citizens. As a result, the quality of living is improved by continuous analyses of data to improve service delivery by governments and other organizations. Due to the presence of extensive devices and data flow over networks, the probability of cyber attacks and intrusion detection has increased. The monitoring of this huge amount of data traffic is very difficult, though machine learning algorithms have huge potential to support this task. In this study, we compared different machine learning models used for cyber threat classification. Our comparison was focused on the analyzed cyber threats, algorithms, and performance of these models. We have identified that real-time classification, accuracy, and false-positive rates are still the major issues in the performance of existing models. Accordingly, we have proposed a hybrid deep learning (DL) model for cyber threat intelligence (CTI) to improve threat classification performance. Our model was based on a convolutional neural network (CNN) and quasi-recurrent neural network (QRNN). The use of QRNN not only resulted in improved accuracy but also enabled real-time classification. The model was tested on BoT-IoT and TON_IoT datasets, and the results showed that the proposed model outperformed the other models. Due to this improved performance, we emphasize that the application of this model in the real-time environment of a smart system network will help in reducing threats in a reasonable time.