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Fahimeh Mohammadi, Maryam Shekofteh, Maryam Kazerani,
Volume 18, Issue 3 (5-2024)
Volume 18, Issue 3 (5-2024)
Abstract
Background and Aim: The growth and development of scientific fields depends on correct and accurate planning and a general and comprehensive understanding of the structure of these fields. Scientific maps are a type of scientometric methods that help to understand the current state of scientific fields and reveal their internal structure. The aim of the present study is to analyze co-authorship and word co-occurrence maps of scientific publications of Iran in the field of endocrinology and metabolism.
Materials and Methods: This is a cross-sectional scientometrics study. The research population is all scientific publications of Iran in the field of Endocrinology and Metabolism on the Web of Science. The co-authorship and co-word maps were analyzed using VOSviewer, Gephi, and NodeXL software. Network analysis was done using social network analysis indicators. Thematic clusters and emerging subjects were also identified through the examination of word co-occurrence networks.
Results: The total scientific publications of Iran in the field of endocrinology and metabolism on the Web of Science was 4847 documents. The co-authorship network is a type of sparse network. The value of the cluster coefficient of this network was 0.212 and its diameter was 11. The average degree of the co-authorship network (6.62) shows that each node is connected with about 6 other nodes on average. The diameter of the co-authorship network is 11. The most productive and influential outhors are Azizi F and Larijani B. Six thematic clusters were identified in the word co-occurrence network, the largest one is oxidative stress and gene expression, followed by the obesity and diabetes cluster. The word “autoimmunity” is one of the emerging words in this field.
Conclusion: Iran’s research in the field of Endocrinology and Metabolism shows an increasing trend, but there is little cooperation between the authors of the field. Their co-authorship networks are sparse, and the authors’ tendency to form clusters is low. Therefore, planning is needed to increase scientific cooperation and the density of networks. It is suggested that the researchers of this field pay attention to the thematic clusters of the co-word network and emerging subjects in the design of their future research.
Materials and Methods: This is a cross-sectional scientometrics study. The research population is all scientific publications of Iran in the field of Endocrinology and Metabolism on the Web of Science. The co-authorship and co-word maps were analyzed using VOSviewer, Gephi, and NodeXL software. Network analysis was done using social network analysis indicators. Thematic clusters and emerging subjects were also identified through the examination of word co-occurrence networks.
Results: The total scientific publications of Iran in the field of endocrinology and metabolism on the Web of Science was 4847 documents. The co-authorship network is a type of sparse network. The value of the cluster coefficient of this network was 0.212 and its diameter was 11. The average degree of the co-authorship network (6.62) shows that each node is connected with about 6 other nodes on average. The diameter of the co-authorship network is 11. The most productive and influential outhors are Azizi F and Larijani B. Six thematic clusters were identified in the word co-occurrence network, the largest one is oxidative stress and gene expression, followed by the obesity and diabetes cluster. The word “autoimmunity” is one of the emerging words in this field.
Conclusion: Iran’s research in the field of Endocrinology and Metabolism shows an increasing trend, but there is little cooperation between the authors of the field. Their co-authorship networks are sparse, and the authors’ tendency to form clusters is low. Therefore, planning is needed to increase scientific cooperation and the density of networks. It is suggested that the researchers of this field pay attention to the thematic clusters of the co-word network and emerging subjects in the design of their future research.
Mohammadreza Asghariyan, Farzad Firouzi Jahantigh,
Volume 18, Issue 3 (5-2024)
Volume 18, Issue 3 (5-2024)
Abstract
Background and Aim: The emergency department of the hospital is considered one of its main entrances; which has provided health care and treatment for critical and non-critical patients and faces various health and treatment restrictions, but the main emphasis is always on resource limitations. Many simulation projects were implemented in hospitals and first in emergency departments with the aim of increasing productivity. The present research is a general description of the patient’s movement flow and length of stay in the emergency department of a selected specialized hospital in Zahedan city. The aim of the current research is to prevent care complications, reduce waiting time and patient stay in the emergency department, present a simulation model and improve it based on discrete-event simulation.
Materials and Methods: Using the data bank of the emergency department system based on the required data and also through the in-person observation of the data related to the duration of the patient’s stay in the emergency department, including the arrival time, waiting time, The type of services provided to the patient, the time of service and the time of departure were collected and checked and confirmed by experts related to this field so that it has the highest level of reliability with the facts. The data were designed in Excel software, and then data analysis and simulation model creation were done using Aren V14 software, and according to the results, the effect of the proposed solutions was evaluated.
Results: The findings of the present research showed that the longest queue created in the emergency department of the selected specialized hospital in Zahedan city is related to medical examination and additional tests. By implementing the simulation model and testing different solutions, solution 3, which means adding one nurse to nursing consultation and one person to radiology, has the most optimizing effect on the performance of the system at different levels of the patient admission process. and the cost of its implementation is more than solutions 1 and 2. This solution created a 14% decrease in the average length of stay and a 28% decrease in the average duration of additional tests.
Conclusion: The use of queuing models and simulation techniques improve the performance of the system and their implementation has significant effects on reducing the waiting time and length of stay of patients in the emergency department, increasing the quality level of the process of monitoring patients. It leads to optimal management of resources and increased productivity.
Zahra Karbasi, Michaeel Motaghi Niko, Maryam Zahmatkeshan,
Volume 18, Issue 3 (5-2024)
Volume 18, Issue 3 (5-2024)
Abstract
Background and Aim: Cataracts are recognized as the cause of 51% of blindness worldwide. Following the promising initial results of artificial intelligence systems in eye diseases, AI algorithms have been applied in the diagnosis of cataracts, grading the severity of cataracts, intraocular lens calculations, and even as an assistive tool in cataract surgery. This study presents a systematic review of AI techniques in the management of cataract disease.
Materials and Methods: This systematic review study was conducted to investigate artificial intelligence techniques to manage cataract disease until November 11, 2023, and based on PRISMA guidelines. We retrieved all relevant articles published in English through a systematic search of PubMed, Scopus, and Web of Science online databases.
Results: In our initial search, 192 records were identified in the databases, and eventually, 23 articles were selected for review. The results indicated that convolutional neural network algorithms (6 articles), recurrent neural networks (1 article), deep convolutional networks (1 article), support vector machines (2 articles), transfer learning (1 article), decision trees (4 articles), random forests (4 articles), logistic regression (3 articles), Bayesian algorithms (3 articles), XGBoost (3 articles), and K-nearest neighbors clustering algorithms (2 articles) were the artificial neural network and machine learning techniques and algorithms utilized. These techniques were employed in the studies for the diagnosis (70%), management (17%), and prediction (13%) of cataract disease.
Conclusion: Various artificial intelligence and machine learning techniques and algorithms can be effective and efficient in diagnosing, grading, managing, and predicting cataracts with high accuracy. In this study, deep learning techniques and convolutional neural networks have made the greatest contribution to cataract diagnosis. Deep learning techniques, decision trees, and Bayesian algorithms were involved in cataract management. Machine learning algorithms such as logistic regression, random forest, artificial neural network, decision tree, K1-nearest neighbor, XGBoost, and adaptive boosting also played a role in cataract prediction. Just as early prediction, diagnosis, and timely referral can reduce future complications of the disease, the use of systems based on artificial intelligence models that have acceptable accuracy can be effective in supporting the decision-making process of physicians and managing this disease.
Materials and Methods: This systematic review study was conducted to investigate artificial intelligence techniques to manage cataract disease until November 11, 2023, and based on PRISMA guidelines. We retrieved all relevant articles published in English through a systematic search of PubMed, Scopus, and Web of Science online databases.
Results: In our initial search, 192 records were identified in the databases, and eventually, 23 articles were selected for review. The results indicated that convolutional neural network algorithms (6 articles), recurrent neural networks (1 article), deep convolutional networks (1 article), support vector machines (2 articles), transfer learning (1 article), decision trees (4 articles), random forests (4 articles), logistic regression (3 articles), Bayesian algorithms (3 articles), XGBoost (3 articles), and K-nearest neighbors clustering algorithms (2 articles) were the artificial neural network and machine learning techniques and algorithms utilized. These techniques were employed in the studies for the diagnosis (70%), management (17%), and prediction (13%) of cataract disease.
Conclusion: Various artificial intelligence and machine learning techniques and algorithms can be effective and efficient in diagnosing, grading, managing, and predicting cataracts with high accuracy. In this study, deep learning techniques and convolutional neural networks have made the greatest contribution to cataract diagnosis. Deep learning techniques, decision trees, and Bayesian algorithms were involved in cataract management. Machine learning algorithms such as logistic regression, random forest, artificial neural network, decision tree, K1-nearest neighbor, XGBoost, and adaptive boosting also played a role in cataract prediction. Just as early prediction, diagnosis, and timely referral can reduce future complications of the disease, the use of systems based on artificial intelligence models that have acceptable accuracy can be effective in supporting the decision-making process of physicians and managing this disease.
Fatemeh Soofiabadi, Alireza Shahraki, Mohebali Rahdar,
Volume 18, Issue 3 (5-2024)
Volume 18, Issue 3 (5-2024)
Abstract
Background and Aim: Given the high sensitivity of the medical field, a mistake can cause irreparable damage to human society. For this reason, finding the symptoms of the disease and the relationships between them to facilitate the improvement of diseases is inevitable. Therefore, the aim of the present study was to first identify the symptoms of neurofibromatosis type 1 by specialists, then determine the relationship between the symptoms and the degree of their impact on each other in order to determine the most important criterion in improving the disease.
Materials and Methods: The present study is of a developmental-applied type in terms of its purpose and of a descriptive-survey type in terms of its data collection method. The case study of the present study is spinal disorders, of which neurofibromatosis type 1 has been diagnosed as one of them based on the opinion of experts. Neurofibromatosis type 1 is a genetic disorder that causes tumors in the nervous tissue. Accordingly, in the present study, the criteria, which are the symptoms of the disease, were first determined using the opinion of a group of experts and the implementation of the fuzzy Delphi method. In the next step, a model for the causal relationships between the symptoms of the disease is presented. For this purpose, a fuzzy cognitive map is drawn using MATLAB, FCMapper and Pajek software, then backward and forward scenarios are presented for neurofibromatosis type 1 and the disease improvement scenario is determined.
Results: The results showed that hormonal changes, flat brown spots on the skin, freckles in the armpit and groin area, soft bumps on the face or under the skin, high blood pressure, respiratory problems, bumps on the iris of the eye (Lish nodules), tumor in the optic nerve-ocular glioma, short stature, bone deformity, learning disabilities-attention deficit hyperactivity disorder (ADHD) and larger than average head size are ranked first to twelfth, respectively. The causal relationships between the symptoms showed that the criterion of hormonal changes has the greatest impact on the criterion of freckles in the armpit or groin area; Therefore, if the hormonal changes criterion improves, neurofibromatosis type 1 will also improve.
Conclusion: The findings of this study have helped the medical community to have a better understanding of the symptoms of the disease so that doctors can improve their prevention and care recommendations based on the severity of the symptoms of the diseases.
Materials and Methods: The present study is of a developmental-applied type in terms of its purpose and of a descriptive-survey type in terms of its data collection method. The case study of the present study is spinal disorders, of which neurofibromatosis type 1 has been diagnosed as one of them based on the opinion of experts. Neurofibromatosis type 1 is a genetic disorder that causes tumors in the nervous tissue. Accordingly, in the present study, the criteria, which are the symptoms of the disease, were first determined using the opinion of a group of experts and the implementation of the fuzzy Delphi method. In the next step, a model for the causal relationships between the symptoms of the disease is presented. For this purpose, a fuzzy cognitive map is drawn using MATLAB, FCMapper and Pajek software, then backward and forward scenarios are presented for neurofibromatosis type 1 and the disease improvement scenario is determined.
Results: The results showed that hormonal changes, flat brown spots on the skin, freckles in the armpit and groin area, soft bumps on the face or under the skin, high blood pressure, respiratory problems, bumps on the iris of the eye (Lish nodules), tumor in the optic nerve-ocular glioma, short stature, bone deformity, learning disabilities-attention deficit hyperactivity disorder (ADHD) and larger than average head size are ranked first to twelfth, respectively. The causal relationships between the symptoms showed that the criterion of hormonal changes has the greatest impact on the criterion of freckles in the armpit or groin area; Therefore, if the hormonal changes criterion improves, neurofibromatosis type 1 will also improve.
Conclusion: The findings of this study have helped the medical community to have a better understanding of the symptoms of the disease so that doctors can improve their prevention and care recommendations based on the severity of the symptoms of the diseases.
Leila Keikha, Fatemeh Sheikhshoaei, Abdolahad Nabiolahi, Mahnaz Khosravi,
Volume 18, Issue 3 (5-2024)
Volume 18, Issue 3 (5-2024)
Abstract
Background and Aim: Health librarians can play an important role in meeting the information needs of the clinical team and improving the quality of medical cares. Increasing clinical health literacy and use of Evidence-based medicine among ophthalmology residents is of great importance due to the importance of patients’ health in this field and appropriate decision-making about the individual’s health status. The present study aimed to evaluate the effect of an educational intervention by clinical librarians on the skills of ophthalmology residents in using of evidence-based information at Zahedan University of Medical Sciences.
Materials and Methods: This was a semi-experimental applied study. The research population was ophthalmology residents of Al-Zahra Eye Hospital, Zahedan University of Medical Sciences during the years 2020-2023, who were selected through a census. During a three-month period, 17 combined training sessions (face-to-face and virtual using the Navid system) were held for 18 ophthalmology residents regarding correct search methods from different databases and appropriate use of evidence-based information. To collect data before and after training, a clinical information literacy questionnaire derived from previous studies was used, and data analysis was performed using SPSS software and ANOVA and ANCOVA statistical tests to compare scores before and after training in the intervention group.
Results: The majority of participating residents (55.6%) were female. Before the intervention, 33.3% of the study population had moderate to high levels of knowledge about evidence-based medicine. There was a statistically significant relationship between the total level of knowledge of residents after training and gender (P-value<0.05). Clinical librarian training was effective on the level of basic knowledge of evidence-based medicine, designing clinical questions, searching for clinical evidence, critical evaluation of clinical evidence, and dissemination of evidence-based medical information of residents (P-value<0.05).
Conclusion: Considering the positive impact of clinical librarians’ intervention in improving the level of clinical decision-making knowledge of ophthalmology residents, it is suggested that evidence-based medicine training workshops or courses be held for residents of different disciplines using a variety of educational methods. In addition, it is suggested that evidence-based units be included in the residents’ curriculum and that training be conducted as a team consisting of medical librarians and specialists and ophthalmologist.
Materials and Methods: This was a semi-experimental applied study. The research population was ophthalmology residents of Al-Zahra Eye Hospital, Zahedan University of Medical Sciences during the years 2020-2023, who were selected through a census. During a three-month period, 17 combined training sessions (face-to-face and virtual using the Navid system) were held for 18 ophthalmology residents regarding correct search methods from different databases and appropriate use of evidence-based information. To collect data before and after training, a clinical information literacy questionnaire derived from previous studies was used, and data analysis was performed using SPSS software and ANOVA and ANCOVA statistical tests to compare scores before and after training in the intervention group.
Results: The majority of participating residents (55.6%) were female. Before the intervention, 33.3% of the study population had moderate to high levels of knowledge about evidence-based medicine. There was a statistically significant relationship between the total level of knowledge of residents after training and gender (P-value<0.05). Clinical librarian training was effective on the level of basic knowledge of evidence-based medicine, designing clinical questions, searching for clinical evidence, critical evaluation of clinical evidence, and dissemination of evidence-based medical information of residents (P-value<0.05).
Conclusion: Considering the positive impact of clinical librarians’ intervention in improving the level of clinical decision-making knowledge of ophthalmology residents, it is suggested that evidence-based medicine training workshops or courses be held for residents of different disciplines using a variety of educational methods. In addition, it is suggested that evidence-based units be included in the residents’ curriculum and that training be conducted as a team consisting of medical librarians and specialists and ophthalmologist.
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