Showing 11 results for Yazdani
Mohammad Reza Meigounpoory, Pedram Yazdani, Hamid Reza Rezaeian Zadeh,
Volume 5, Issue 5 (7 2012)
Abstract
Background and Aim: Identification of entrepreneurial opportunities in the field of nutrition counseling is a central issue in employing nutrition consultants and meeting the needs of patients with chronic diseases better. To this end, the present survey has been conducted in order to analyze the supply and demand trends of diabetes nutrition counseling as a basic step toward identifying the entrepreneurial opportunities for nutrition consultants in Tehran.
Materials and Methods: To implement this descriptive-survey study, a questionnaire based on Likert scale was sent by email to 80 active professionals in the field of nutrition counseling services in Tehran, of whom 48 responded to its questions. Then, using SPSS11.5, the mean values of participants' responses were calculated and compared with one another.
Results: The findings obtained based on participants' responses revealed that the need for "nutritional counseling in the form of a treatment team" was mostly not met in different age, education and income groups of diabetic patients
Conclusion: "Nutrition counseling in the form of a treatment team" can be considered as a suitable field for entrepreneurial activities.
Pedram Yazdani, Hamidreza Rezaeian Zadeh, Mohammadreza Meigounpoory,
Volume 6, Issue 3 (7 2012)
Abstract
Background and Aim: Nowadays, because of the intense competition between nutrition consultants and rapid changes in patients' demands for nutrition counseling, application of the concepts such as "Entrepreneurial Opportunities Recognition" in this field seems unavoidable. One of the most commonly used methods for recognition of entrepreneurial opportunities is "Strategic Analysis of External Environment". Present survey has been conducted in order to study the impact of nutrition consultants' educational level on their strategic analysis about entrepreneurial opportunities.
Materials and Methods: This analytical survey was implemented based on the data (nutrition consultants' educational levels and their responses regarding the supply and demand for nutrition counseling services) which was collected during a previous study entitled ((Analysis of supply and demand trends in the field of diabetes nutrition counseling: A basic step towards identifying the entrepreneurial opportunities for nutrition consultants)). Also, ((Spearman's correlation test)) and ((GLM multivariate test)) were performed for the analysis of aforementioned data.
Results: Both statistical tests showed that nutrition consultants' educational levels (including 1- Bachelor of Science and 2- Master of Science and PhD) had significant relationship with two-thirds of their comments.
Conclusion: Nutrition consultants' educational level has a relative impact on their strategic analysis about the entrepreneurial opportunities.
Ali Abedini, Hamid Reza Irani, Hamid Reza Yazdani,
Volume 13, Issue 1 (Apr & May 2019)
Abstract
Background and Aim: In our country because of the security of production and distribution of medicine, Pharmaceutical producers and distributors are known for profitability. The weaknesses in this industry include low productivity in the raw material supply, inefficiency in Pharmaceutical distribution and increasing corporate finance costs. Therefore, the purpose of this research is to identify and prioritize the critical success factors in SCM and distribution of the pharmaceutical industry in the country to provide effective decision making in this field.
Materials and Methods: The research consists of two phases of library and surveying. In the first phase, by searching in scientific databases, CSFs in the supply chain and distribution were identified and were categorized in 25 dimensions. Based on the Pareto principle 9 dimensions out of 25 divided dimensions became the pairwise comparison in DEMATEL method to determine the impact and effectiveness. The statistical Society of this research is pharmaceutical producers and distributors in 2018. We have used 13 experts in marketing, SCM, and distribution of pharmaceuticals companies as samples. For data analysis, Excel and MATLAB software were used.
Results: Senior management commitments, use of information technology and government intervention were the first three influential factors and processes, service quality and trust were the first three effective factors. Also, the most challenging factor was the senior management commitment and the least interactive factor was government intervention.
Conclusion: Managers can not consider all the factors; they should invest in influential and challenging factors.
Azita Yazdani, Ali Asghar Safaei, Reza Safdari, Maryam Zahmatkeshan,
Volume 13, Issue 3 (Aug & Sep 2019)
Abstract
Background and Aim: Breast cancer is the most common type of cancer and the main cause of death from cancer in women worldwide. Technologies such as data mining, have enabled experts in this area to improve decision making in the early diagnosis of the disease. Therefore, the purpose of this research is to develop an automatic diagnostic model for breast cancer by employing data mining methods and selecting the model with the highest accuracy of diagnosis.
Materials and Methods: In this study, 654 available patient records of Motahari breast cancer Clinic in Shiraz" were used as the sample. The number of records was reduced to 621 after the pre-processing operation. These samples had 22 features that ultimately used ten were used as effective features in the design of the model. Three types of Decision tree, Naive Bayes and Artificial neural network were used for diagnosis of breast cancer and 10-fold cross-validation method for constructing and evaluating the model on the collected data set.
Results: The results of the three techniques mentioned all three models showed promising results in detecting breast cancer. Finally, the artificial neural network accounted for the highest accuracy of 94/49%(sensitivity 96/19%, specificity 86/36%) in the diagnosis of breast cancer.
Conclusion: Based on the results of the decision tree, the risk factors such as age, weight, Age of menstruation, menopause, OCP of records duration, and the age of the first pregnancy were among the factors affecting the incidence of breast cancer in women.
Ali Abedini Abedini , Hamidreza Irani, Hamidreza Yazdani,
Volume 13, Issue 6 (Feb & Mar 2020)
Abstract
Background and Aim: Due to the variety of herbal medicine products and brands, competition among herbal medicine manufacturing companies has become a scientific and tactical competition. Herbal medicine companies by identifying the problems of pharmaceutical distribution companies can evaluate them and find solutions to their problems and finally, they can maintain their competitive advantage in the market. Therefore, the purpose of this study was to identify the marketing channel problems of herbal medicine from the perspective of pharmaceutical distribution companies.
Materials and Methods: In this research, with exploratory interviews, the marketing channel problems of herbal medicine were identified from the perspective of distributors and analyzed using content analysis method. The statistical population of this study was pharmaceutical distributor’s managers in Tehran provinces, among which 16 persons were selected through the judgmental and snowball sampling method.
Results: The results showed the marketing channel problems of herbal medicines were categorized in Product, Prices, Place, Promotion, Physical Evidence, Process and People.
Conclusion: The government and the laws, in addition to the marketing can affect the marketing channel problems of these drugs from the perspective of distributors.
Azita Yazdani, Reza Safdari, Roxana Sharifian, Maryam Zahmatkeshan, Marjan Ghazi Saeedi,
Volume 14, Issue 2 (Jun & Jul 2020)
Abstract
Background and Aim: When clinical decision support systems are developed, implementing solutions that enable these systems to be -used on a large scale can reduce the production costs associated with the creation, maintenance and by sharing these systems, producing multiple clinical decision support systems will be prevented. In recent years, one of the approaches used for this purpose in combination with clinical decision support systems is the service-oriented architecture approach. The purpose of this study was to investigate the role and importance of service-oriented architecture in delivering scalable architectures of clinical decision support systems focusing on different approaches to this architecture.
Materials and Methods: This article is a simple review article. Bibliographic databases of IEEE Explore, Science Direct, Springer, Web of Science, and Scopus were reviewed. The keywords "Service Oriented Architecture" and "clinical decision support systems" were used as keywords along with related terms for searching these databases.
Results: The clinical decision support systems based on service-oriented architecture brings benefits such as Facilitate knowledge maintenance, reducing costs and improving agility. Point-to-point communication, enterprise service bus, service registry, clinical and engine guiding engine, and service choreography and orchestration are general architectural designs that are evident in the use of web-based clinical decision support systems based on a service-oriented architecture approach.
Conclusion: Service-oriented architecture is a potential solution for delivering scalable platforms for clinical decision systems.
Ali Reza Heidarian Naeini, Ghahraman Mahmoodi Alemi, Jamshid Yazdani Charati,
Volume 15, Issue 3 (Aug & Sep 2021)
Abstract
Background and Aim: In recent years, the family physician plan has been implemented as a main strategy of health system in Iran. Therefore, the necessity to reform organizational structure based on experiences of other countries is felt more than before. The aim of this study was to explore required structures of Family Physician Program to achieve service quality dimensions in Primary Health care through analyzing country experiences.
Materials and Methods: This study was a systematic review. All relevant databases were searched using appropriate search strategies and keywords (Family Physicians, Primary Health Hare, Quality of Care). To evaluate the quality of selected papers, CASP tool was applied by 4 experts, and their choices were discussed to reach a final decision.
Results: In order to achieve the quality of services in the field of family medicine, based on the findings of this study, eight important executive structures must be considered. These structures are: Organizational and managerial structures, including health system governance, Support mechanisms and referral system, Systematic communication platform, Electronic health services, Service delivery processes,Insurance structure, Supervisory and control structure including financial control mechanisms, competitive control and quantitative control, Payment structure, quantitative and qualitative development structure of service providers, Quality structure that includes the definition and evaluation of quality and accountability mechanisms as well as incentive mechanisms for service quality, Support structure including insurance support, classified support for specific groups, and finally, the cultural structure in the two areas of culture building of the referral system and strengthening the position of family physicians.
Conclusion: Quality improvement in primary health care requires attention to executive structures. Use of executive experiences of other countries will be useful in achievement of quality health care in family physician system.
Leila Erfannia, Azita Yazdani,
Volume 17, Issue 3 (8-2023)
Abstract
Background and Aim: With the spread of the Corona pandemic, the statistics of the number of mobile health applications have grown significantly. This research was conducted with the aim of evaluating the content of Persian language applications in the management of Covid-19.
Material and Methods: In this review research, a systematic search for Persian language programs in the field of Covid-19 management was conducted in four mobile application markets including Myket, Bazzar, Google Play and App Store. The content of the programs was evaluated based on a researcher-made checklist, which was verified according 3 specialist comments, in the five axes of ease of use, education, monitoring, privacy and data sharing. Programs that received more than 50% of the evaluation score were introduced as selected programs. By removing duplicate programs, 119 programs were extracted, of which 21 programs entered the final stage of quality evaluation based on the inclusion and exclusion criteria and after a complete review of the content and capabilities.
Results: Based on the total points of the program, Safiran Salamat received the most score (31), Ac19 and mask were ranked next with 27 and 22 points, respectively. These three programs along with Corona Amar Tashkhis as fourth program received more than 50% of the content review and 17 other programs received less than 50% of the total score. Government has a great role in programs development (three program were government and one was non- government base). All 4 programs, had acceptable score in ease of use but none of them develop for user tracking. Pearson’s correlation test was used to test the relationship between the quality (total scores of apps) and the popularity (amount of downloads), and no significant correlation was observed.
Conclusion: The results of the present study showed that Iranian mobile applications have an acceptable performance in the fields of education and information sharing, but their low popularity makes the achievement of these goals far from expected. Marketing strategies can be effective as one of the useful policy in increasing the use of mobile health programs. Also, the inclusion of capabilities such as contact tracing and online consultations can be fruitful in the pursuit of goals.
Zahra Khaje, Kamran Yazdani, Ibrahim Abdollahpour, Mohsen Mohammadi, Saharnaz Nedjat,
Volume 17, Issue 5 (12-2023)
Abstract
Background and Aim: Alzheimer’s is a chronic disease that causes cognitive disabilities, thinking, personality changes and disruptions in daily activities. Due to these disorders, patients need long-term care. Most care for Alzheimer’s patients is done at home by family members, which makes home caregivers mentally, physically, emotionally, socially and financially vulnerable. Health personnel have a key role to play in providing information and guidance and helping the family control these conditions. The purpose of this study was to examines the level of knowledge and attitude of health workers and determines the related factors.
Materials and Methods: This research is a cross-sectional study to evaluate the level of knowledge and attitude of health workers about Alzheimer’s disease and its related factors. All 260 health workers of Gorgan and Kordkuy districts were studied by census method to assess their knowledge and attitude about Alzheimer’s disease and its related factors. ANOVA and T-tests were used to determine the relationship between the independent variables and the dependent variable. Variables whose significant level of correlation with response variable was less than 0.2 in bivariate analysis were entered into the regression model and finally multiple linear regression was used to determine the factors related to level of knowledge and attitude.
Results: The mean level of knowledge was 46.73% (95% CI, 45.46 to 48.16) and the mean level of attitude was 55.61% (95% CI, 54.63 to 56.74). The results show that those with a history of previous education, a history of caring for Alzheimer’s patients, a higher level of work experience in the health care system, and having a female gender and be married have higher levels of knowledge and those with a history of previous education and Sistani descent had a higher attitude.
Conclusion: In general, the mean level of knowledge was 46.73(0-100) and the mean level of attitude was 55.61(0-100). Factors such as: gender, work history in the health system, history of participating in educational workshops, history of caring for sick patients, and marital status were related to the level of knowledge and factors such as ethnicity and history of participating in the training workshop were related to the level of attitude of the health care providers.
Miss Fariba Moalem Borazjani, Azita Yazdani, Reza Safdari, Seyed Mansoor Gatmiri,
Volume 17, Issue 6 (2-2024)
Abstract
Background and Aim: Kidney failure is a common and increasing problem in Iran and worldwide. Kidney transplantation is recognized as a preferred treatment method for patients with end-stage renal disease (ESRD). Machine learning, as one of the most valuable branches of artificial intelligence in the field of predicting patient outcomes or predicting various conditions in patients, has significant applications. The purpose of this research was to predict kidney transplant outcomes in patients using machine learning.
Materials and Methods: Since CRISP is one of the strongest methodologies for implementing data mining projects, it was chosen as the working method. In order to identify the factors affecting the prediction of kidney transplant outcomes, a researcher-created checklist was sent to some of nephrologists nationwide to determine the importance of each factor. The results were analyzed and examined. Then, using Python language and different algorithms such as random forest, SVM, KNN, deep learning, and XGBoost the data was modeled.
Results: The final model was multilabel, capable of predicting various kidney transplant outcomes, including rejection probability, diabetic reactions, malignant reactions, and patient rehospitalization. After modeling the input data features, the model was able to predict the four kidney transplant outcomes such as rejection, diabetes, malignancy and readmission with an error rate of less than 0.01.
Conclusion: The high level of accuracy and precision of the random forest model demonstrates its strong predictive power for forecasting kidney transplant outcomes. In this study, the most influential factors contributing to patient susceptibility to the mentioned outcomes were identified. Using this machine learning-based system, it is possible to predict the probability of these outcomes occurring for new cases.
Sara Hashemi, Shahla Faramarzi, Laya Rahmani Pirouz, Azita Yazdani,
Volume 18, Issue 2 (5-2024)
Abstract
Background and Aim: Burn injury are one of the most common traumas worldwide and the sixth leading cause of death in Iran. The challenges related to the survival rate of burn patients, as well as the associated mortality cases, have led to advancements in the identification of risk factors. Early detection and recognition of these risk factors are essential, and the provision of predictive models can be beneficial. This research was conducted with the aim of reviewing the effectiveness of artificial intelligence in predicting survival in burn patients.
Materials and Methods: This study was a systematic review. A comprehensive search of Scopus, PubMed, IEEE, and Web of Science databases was conducted from inception to July 2023 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Keywords and Mesh terms related to burn, artificial intelligence, survival and prediction were used in the search.
Results: Out of 3599 identified studies, only nine were included in the analysis. Based on the articles reviewed, the effective factors in predicting survival or mortality in burn patients were classified into four main categories: demographic, clinical, tests and co-morbidities. Some of the known effective factors in patient survival, which have been examined in over 40% of studies, include age, gender, total body surface area, inhalation injury, and type of burn. The results showed that in the studies reviewed, the volume of the smallest dataset used in the analyses was 92 samples. In contrast, the volume of the largest dataset used was reported to be 66,611 samples. Among these studies, 33% have indicated that artificial neural network algorithms and random forest show the best performance. The criteria used to evaluate the models in the retrieved studies are diverse.
Conclusion: The use of machine learning algorithms in predicting the survival of burn patients is promising. The results obtained from identified influential factors can assist data science researchers in the data understanding phase and can serve as a roadmap in collecting the initial dataset.
