fuzzy logic

Although healthcare is a key determinant of health status, in recent years, the role of non-medical factors in health has become more prominent, and policymaking in the field of social determinants of health has been recognized as one of the most important areas for improving community health. This study aimed to identify and analyze social determinants of health in Iran.

The present study used mixed-methods approach. First, the qualitative approach was conducted using Graneheim and Lundman’s six-step method to analyze the content of 58 sources and identify social determinants of health at the micro level. Subsequently, the quantitative Fuzzy Delphi method and Fuzzy Cognitive Mapping (FCM) were used to determine priorities and interactions. To this end, first, the factors identified by 15 academic and executive experts in the field of health were screened using the Fuzzy Delphi method, and then 6 of these experts were purposefully selected to determine the relationships between the factors.

The 83 identified determinants were classified into two categories: individual level with seven subcategories (family status, healthy start to life, childhood development, social gradient, individual behaviors, spiritual and mental health, and lifelong programs); and local level with four subcategories (environmental and chemical, physical environment, urban infrastructure, and social environment). After drawing a fuzzy cognitive map and determining causal relationships between factors, social environment (centrality degree = 12.10) and individual behaviors (centrality degree = 11.43) were found to be the most influential factors.

The findings of this study, by providing a fuzzy map, identified quantitative relationships between factors and helped policymakers understand priorities and linkages between sectors for the development and implementation of health-oriented policies in all sectors.

Storage tanks are crucial in safety within industrial complexes. They can lead to harm to people and financial losses during accidents. Developing a more accurate method to identify pivotal accident causes can greatly enhance the prevention strategies.

Initially, a preliminary hazard identification from the storage tank was conducted, and tank leakage event was chosen as the top event. Next, bowtie and then a Bayesian network were constructed, and relationships between nodes were input into the software. The probabilities of nodes were estimated using expert opinions and an improved similarity aggregation method. Finally, considering the top event as the evidence node, RoV values were computed to prioritize the most important events

This study identified 53 basic events. The probability of a leakage event from the LPG storage system was 1.5E-02, with the most likely hazardous outcome being pool fire.
Using RoV calculation, some basic events like inadequate connection leaks, natural disasters, structural defects in valves and concrete wall were identified as the most critical nodes. Finally, preventive and mitigative recommendations were suggested for them.

Bayesian network is known as an effective tool for modeling cause-effect and integrating this approach with fuzzy logic and improved similarity aggregation method can reduce uncertainty in studies. Additionally, their ability for probability updating enables prioritization of important events. The results of such prioritization can serve as a valuable guide for optimizing preventive maintenance activities.

Gastrointestinal ulcers occur due to an imbalance between the defense mechanisms of the gastric mucosa and damaging forces, especially gastric acid and pepsin. Overall, complications occur in 10%-20% of these patients, and 2%-14% of wounds eventually perforate. The use of non-steroidal anti-inflammatory drugs, steroids, smoking, Helicobacter pylori and high salt diet can be mentioned as important etiologies in this regard.

In this study, the information of patients with peptic ulcer who referred to Beheshti Hospital from 2019 to 2022 was analyzed. They were divided into two groups with perforation and without perforation. Then the variables of age, sex, smoking, NSAID and opium use, Helicobacter pylori infection and proton pump inhibitor use and previous history of peptic ulcer were investigated in two groups.

The findings of the present study showed that the average age in the group with perforation was 48.7 and in the group without perforation was 42.04. In the non-perforated group, 58.5% of the patients were male, and in the group with perforation, 82.2% of the patients were male. In terms of smoking, 29.6% were smokers in the group without perforation and 50.4% were smokers in the group with perforation. Opium consumption was about 15.6% in people without perforation and about 33.3% in people with perforation. In terms of NSAID use, the prevalence was 35.6% in the group without perforation and 27.4% in the group with perforation. PPI consumption was 46.7% in the group without perforation and 21.5% in the group with perforation. In terms of the prevalence of H.pylori infection, the prevalence in the non-perforated group was 45.2% and in the perforated group it was 30.4%. The previous history of PUD was 56.3% in the non-perforated group and 37.8% in the group with perforation.

There was a significant difference between cigarette and opium consumption in the perforated and non-perforated groups, and PPI consumption in these two groups. In general, the prevalence of PUD was higher in males in both perforated and non-perforated types. Fuzzy results also confirmed the effect of risk factors concordance with perforation.

The accurate evaluation of error probability and risk is important. Accordingly, this Comparative study was conducted to evaluate the risk of human error in emergency situations using SLIM and Fuzzy SLIM techniques in fierfighting tasks.

This cross-sectional and descriptive-analytical study was conducted among 12, using Fuzzy SLIM and SLIM techniques. 39 sub-tasks were studied in 4 phases (Awareness, Evaluation, Egress and Recovery). Considering the advantages of the Fuzzy SLIM method, fuzzy logic was used in weighting of performance shaping factors (PSF). Excel software was used to calculate the probability of error. Also, correlation and kappa statistical tests were used for data analysis in SPSS software.

The mean and standard deviation of human error probability in different sub-tasks of firefighting in SLIM and Fuzzy SLIM methods were 0.095357 ± 0.026193 and 0.06490 ± 0.051748, respectivly. In 48.7 percent of the sub-tasks, the probability category of human error and the assessed risk were the same; however, in 89.7 percent of the sub-tasks, the estimated level of risk was the same in both methods. Correlation test showed that the correlation coefficient of error probability values between the two methods was 0.32, which indicated a moderate correlation in this regard. Additionally, the results of kappa statistical test for the estimated level of risk showed that there is a high agreement between Fuzzy SLIM and SLIM (P value <0.05).

The results of the study indicated meaningful agreement and a moderate correlation between Fuzzy SLIM and SLIM. Therefore, due to the relatively high accuracy of Fuzzy logic methods, and also the long steps of implementing the SLIM method, the Fuzzy SLIM method can be a good alternative to this method.

The healthcare referral system is one of the main factors for improving the health conditions, especially in developing countries. The present study aims to investigate the main factors for the improvement of the family physician referral system in Bojnord, Iran.

This is an analytical (quantitative-qualitative) survey that was conducted in 2021. In the qualitative phase, the related components were first extracted through interviews with a panel of experts (n=24). Then, a questionnaire was created based on the extracted items (n=30). In the quantitative phase, according to the experts’ opinions, the items were weighted using the fuzzy hierarchical analysis, where higher values indicate higher weight. The reliability of the questionnaire was evaluated using Cronbach’s α coefficient in SPSS software. Also, AHP software was used to perform the fuzzy hierarchy analysis.

Twenty components were identified by experts, which were divided into three categories including organizational/managerial requirements, human resources management requirements, and information technology requirements, with the weight values of 0.486, 0.213 and 0.301, respectively, indicating that organizational/managerial requirements were more important. Among the organizational/managerial requirements, the component of “design and implementation of service quality improvement cycles in the referral system” had the highest weight (0.159). Among the human resources management requirements, the component of empowering human resources had the highest weight (0.412). Among the information technology requirements, improvement of the information technology infrastructure had the highest weight (0.372).

The use of clear procedures and regulations, the digitalization of the referral system, and the staff training are among the very important factors that can lead to improving the referral system in Bojnord City.

Storage tanks are crucial in safety within industrial complexes. They can lead to harm to people and financial losses during accidents. Developing a more accurate method to identify pivotal accident causes can greatly enhance the prevention strategies.

Initially, a preliminary hazard identification from the storage tank was conducted, and tank leakage event was chosen as the top event. Next, bowtie and then a Bayesian network were constructed, and relationships between nodes were input into the software. The probabilities of nodes were estimated using expert opinions and an improved similarity aggregation method. Finally, considering the top event as the evidence node, RoV values were computed to prioritize the most important events

This study identified 53 basic events. The probability of a leakage event from the LPG storage system was 1.5E-02, with the most likely hazardous outcome being pool fire.
Using RoV calculation, some basic events like inadequate connection leaks, natural disasters, structural defects in valves and concrete wall were identified as the most critical nodes. Finally, preventive and mitigative recommendations were suggested for them.

Bayesian network is known as an effective tool for modeling cause-effect and integrating this approach with fuzzy logic and improved similarity aggregation method can reduce uncertainty in studies. Additionally, their ability for probability updating enables prioritization of important events. The results of such prioritization can serve as a valuable guide for optimizing preventive maintenance activities.

Anesthesiology experts are exposed to significant risks due to advancements in anesthesia techniques and surgery, as well as the emergence of modern equipment and newer drugs, along with increased responsibilities and expectations. Therefore, the present study was conducted with the aim of providing a model for assessing the risks and hazards existing in this profession.

Initially, various sources, including interviews with experts and reviews of articles, were used to extract occupational risks in the field of nurse anesthetist. Subsequently, the study criteria, including consequence, probability, and exposure, were determined, and the weighting of these criteria was calculated using AHP.Finally, the prioritization of different risks was determined using CoCoSo.

Based on the results of determining the weights of criteria using the AHP, it was identified that the consequence with a weight of 0.43 is the most important criterion, while the exposure criterion with a weight of 0.27 is the least important criterion in this study. Also, among the investigated risks, inhaling anesthetic gases while wearing a mask, filling a vaporizer(R5) was selected as the most dangerous risk, followed by skin contact with scrubs or blood, patient's secretions or infected tissues(R1), needle sticking during venipuncture,patient's spinal anesthesia(R12), which were selected as the second and third most dangerous risks in anesthesiology experts job, respectively.

In this study it was established that the proposed method can be used for risk assessment and prioritization of occupational hazards in the job of a nurse anesthetist as well as other professions

Statistics show that occupational accidents in the construction industry are relatively high. Also, a significant percentage of these accidents in this industry leads to death. Therefore, in this study, the parameters affecting the causes of occupational accidents in the construction industry were evaluated using the combined method of Fuzzy logic-AHP and Pareto's theory in Kurdistan province, and management and engineering mitigation measures were determined.

Methods and Materials: In this study, the effective parameters in the occurrence of accidents in the construction industry were identified and a questionnaire based on fuzzy hierarchical analysis was designed and distributed, which was completed by 83 occupational health and safety specialists in the Kurdistan province of Iran. The data analysis was done based on Fuzzy logic-AHP and Pareto theory.

 In the evaluation of this study, out of the set of eight factors, unsafe conditions were found to be the most influential factor. Also, among the sub-factors of this index, the two parameters of lack of scaffolding and safety equipment for working at height and lack of protective fences in existing cliffs were the most important.

 Based on these results, it is suggested that to reduce the occurrence of accidents in this industry, control measures to create safe working conditions at height should be prioritized. Also, paying attention to the parameters determined by the Pareto theory in each factor can easily reduce more than 80% of the causes of occupational accidents in this industry.

Failure Mode and Effects Analysis (FMEA) is a structured way to find and understand the states of a system’s failure and to calculate the resulting effects. In this method, which has been criticized by many researchers, the risk priority number is obtained for each failure mode based on the multiplication of the three parameters of occurrence (O), severity (S) and detection (D). In order to overcome the disadvantages of the traditional method of FMEA, such as ranking the failure and weighting the parameters, this research proposes a model in the fuzzy set.

The model proposed in this paper is a nonlinear model for weighting the parameters of the FMEA and the revised TOPSIS method for ranking the failures, which is used for the first time to improve the FMEA method.

The proposed model was presented in the Copper Complex of Shahr-e-Babak to assess safety risks. Based on the results of the study, it was found that in this proposed model, the weights of severity and detection were 0.479 and 0.186, respectively, and the results of the ranking showed that the risks of falling from height and getting stuck between objects had the highest and lowest priorities, respectively.

In the proposed model, based on Logarithmic Fuzzy Preference Programming and the revised TOPSIS method, the definite weights of the parameters were presented without any fuzzy number ranking and risk ranking with more criteria, respectively. Therefore, the proposed model has a higher ability compared to the traditional FMEA, and its application can be recommended to determine the ranking of risks.

Building clinical decision support models to automatically extract knowledge from data helps physicians in early diagnosis of disease. Interpretability of the diagnostic rules of these models for understanding how they make decisions and increasing confidence in their output is a key indicator in determining their efficacy.

In this retrospective study, an automated hybrid rule extraction model is proposed for type 2 diabetes. In order to evaluate the model, the PIMA Diabetes dataset including 768 records and 9 variables was used. After removing the missing and outlier data in the data preprocessing stage, a proposed fuzzy-genetic hybrid model was implemented using MATLAB software to extract the rules. A self-organizing chromosomal structure was used to eliminate the complexity of setting genetic algorithm operators and facilitate the re-implementation of the model in other applications.

The accuracy of the proposed model on the PIMA dataset was 79.05%. This accuracy was obtained by two fuzzy rules, each of which contained only two independent variables. In addition, two single diagnostic rules for diabetic and non-diabetic individuals were presented with accuracy of 70.83% and 81.48%, respectively. The number of pregnancies, body mass index, diastolic blood pressure, diabetes pedigree function, plasma glucose concentration, and triceps skinfold thickness were the most effective factors in having or not having diabetes in the extracted rules.

The proposed model with high accuracy and interpretability is quite suitable in producing an accurate and highly interpretable set of rules as well as single rules for diagnosing diabetes or absence of diabetes. Due to its self-organizing ability, it can also be used for other binary classification purposes.

Identification of competencies is necessary base for a succession plan and efficient human resource management. Studies show that this important issue has been neglected by using the competency model at the middle-level nurse managers.
Since, the methods based on fuzzy logic provide the possibility of mathematical formulation of ambiguous concepts, such as competence. The aim of this study was to design a competency model for middle-level nurse managers using FDM in the second half of 1399.

This study was conducted according to health system management studies. The population included all managers of nursing services (matrons) of hospitals affiliated to Tehran University of Medical Sciences. Census sampling used and research samples included 14 nursing service managers (as experts). After reviewing literatures, an initial conceptual model was designed. In the following, two next steps were conducted as a survey form was used to receive the opinions of experts confirming the model leveling. In the second step, the indicators were screened using the FDM.

All participants were female and 9 people had a master degree. The experts' agreement with the model leveling was 90%, which indicated that it was approved. In FDM, the indicators with crisp values higher than 0/7 were confirmed by experts. The design model consisted of 3 dimensions technical, relational, perceptual, 6 components, 14 indicators and 28 sub-indicators.

This study showed that the criteria for selecting middle-level nurse managers include various dimensions of competence including technical, relational and perceptual. Senior managers can use the results of this research to achieve an appropriate planning for succession plan.

Emergencies are unforeseen and unpredictable situations. In these situations, people’s performance is affected by various factors that cause stress. People’s performance in such situations can also affect human error probability. The purpose of this study was to evaluate human error in emergency situations based on the fuzzy CREAM and Fuzzy Analytical Hierarchy Process (FAHP).

This descriptive-analytical study was performed in a petrochemical industry in Markazi province in 2019. The FAHP was used to prioritize emergency situations. To evaluate human error in these conditions, the weights of Common Performance Conditions (CPC) was determined using Analytical Hierarchy Process (AHP) method. Human error probability was calculated using a fuzzy CREAM method in the most important emergency situations.

The results of the FAHP showed that “Hydrogen leak from the cylinder joints in the olefin unit” was the most important emergency. The highest relative weight was related to crew collaboration quality (0.06) in the emergency situation.

This method can also be used to identify the important factors in human error occurrence and high weighted CPCs and plan to control them.