Showing 4 results for Hematology
Daneshmand Ar, Forouzandeh H, Azadi M, Cheraghzadeh Dezfuli S,
Volume 7, Issue 2 (1-2015)
Abstract
Background: This study made an attempt to make the quantitative and qualitative evaluation of hematological research output in five Islamic countries Iran, Turkey, Malaysia, Saudi Arabia and Egypt which have the most scientific productions from 1996-2013.
Materials and Methods: The current study was carried out during the 1st to 31st of September, 2014 in Blood Transfusion Research Center, Shiraz, Iran. This bibliometric study evaluated quantities and qualities of publications on hematological researches based on SCImago Journal Ranking, for over 17 years (1996- 2013). Strategy of the research was based on the keyword “hematology “. Neither language nor document type restrictions were considered. Data were extracted, tabulated, and compared to identify the ranks as well as trends. The ranking and analyzing indicators included were: ‘number of documents’, ‘citable documents’, ‘citation’, ‘self-citation’, ‘cites per documents’, ‘H-index’, ‘sited documents’, and ‘international collaboration’ .
Results: The 5 Islamic countries published a total of 6914 documents in the field of hematology in this period. This number represents 0.248 % of the total documents produced globally in the field of hematology. Results revealed an increase in the number of publications and citable documents for these countries during 1996-2013. Comparison among these countries showed that Turkey, Iran, Egypt have the highest number of documents and citable documents, respectively. Furthermore Turkey and Iran led qualitative indicators like H-index and citation.
Conclusion: Despite considerable improvement in recent years these Islamic countries should further support their scientific institutes to increase the quantity and quality of hematology publications.
Keywords: Islamic countries, hematology, SCImago Journal Ranking, bibliometric study.
Babak Abdolkarimi, Hassan Abolghasemi, Mohammad Faranoush, Peyman Eshghi, Shahin Shamsian, Mahdi Shahriari, Bijan Keikhaei, Nader Momtazmanesh, Arash Alghasi, Fatima Malek,
Volume 13, Issue 4 (12-2021)
Abstract
Background: Educational evaluation is a broad concept that is related to all elements of the educational system. This concept is the result of the interaction of all values that are implemented with different titles and forms inside and outside the educational system to increase the performance of the educational system. The field of pediatric hematology and oncology is one that is constantly evolving due to extensive and numerous researches in various fields. These changes must be in line with changes in the health care delivery system. In this study, among the decision models, the CIPP model which is an evaluation model for curriculum evaluation given by Stufflebeam in 1983 which includes four elements: C- Context, I- Input, P- Process and P- Product., was selected to evaluate the educational curriculum of Iranian pediatric hematology and oncology fellowship.
Methods: The present study has two quantitative and qualitative aspects and a quantitative cross-sectional, descriptive-analytical study. This analysis was conducted in 2021 by the strategic group of the Iranian pediatric hematology and oncology association. Its statistical population consisted of members of this association. Most members have a degree in pediatric hematology and oncology. The research was conducted by census method. Data were collected using a researcher-made questionnaire. In general, the training course was examined in 4 areas of education and research, hardware facilities of the training and current environment, and professional abilities other than the content of the course. The efficacy of the evaluation questionnaire of the Pediatric hematology and oncology fellowship course was a combination of open and closed questions based on the “Kirk Patrick evaluation model”. This questionnaire had 20 questions. The internal evaluation based on Cronbach’s alpha was 0.92. The items surveyed in the questionnaire were: learning modern medical principles such as evidence-based medicine and clinical reasoning, learning the principles of medical ethics, study skills, understanding of legal procedures, ability to electronically research and adequacy of educational subjects. Satisfaction of the faculty members, students ‘satisfaction and the need for supplementary courses, the current method of evaluating students’ communication skills, the scientific ability of the eligible faculty in teaching current topics and participation in educating the students were among the other items of the questionnaire.
Results: In the internal validity study, Cronbach’s alpha coefficient of 0.92 was obtained for the current situation and 0.96 for the optimal situation. Descriptive statistics (mean and standard deviation) and one-group and independent t-test were used to analyze the data. Findings indicated that there was a significant difference between the current and desired status of free and absentee university exams in the areas of purpose, design, implementation, modification and feedback and the three components of each of these axes. According to the obtained results, changing the current educational curriculum of the subspecialty fellowship in pediatric blood and cancer is necessary and inevitable.
Conclusion: Corrective suggestions for writing a new curriculum in accordance with modern sciences and medical needs of the country were extracted and applied in the new curriculum.
Mahdi Shahriari,
Volume 14, Issue 1 (3-2022)
Abstract
Thalassemia minor (MT), either β or α, is the most frequent single gene mutation in human beings affecting 6% of population worldwide. It is more frequent in the Mediterranean region, Africa and South-East Asia. On the other hand Iron deficiency anemia (IDA), either nutritional or secondary to gastrointestinal or menstrual bleeding, is the most frequent cause of acquired anemia. Differentiation between these two types of anemia is important because both of them present themselves as microcytic anemia.2-6 However, in the thalassemia belt, some patients may have mixed IDA and TM.Therefore discrimination indexes should discriminate three categories from the normal population and each other.
Rahime Tajvidi Asr, Milad Rahimi, Mohammad Hossein Pourasad, Salar Zayer, Mohammadreza Momenzadeh, Mustafa.ghaderzadeh@gmail.com Mustafa Ghaderzadeh,
Volume 16, Issue 4 (12-2024)
Abstract
Introduction: The field of hematology faces significant challenges in data analysis, especially in the diagnosis and prediction of diseases. Traditional methods of analysis are often time-consuming, complex, or inadequate to handle the complex nature of blood-related data. This requires the development of advanced techniques for accurate prediction and classification. Artificial Intelligence (AI)-based methods have emerged as a powerful solution that enables more efficient and accurate analysis of hematological data. This study aims to systematically review published research on the use of different artificial intelligence algorithms in the analysis of this field of data.
Methods: Using a combination of keywords related to blood data analysis and artificial intelligence, we searched medical and scientific databases to identify relevant articles. A data extraction form was developed to collect relevant information from selected studies based on predefined inclusion and exclusion criteria. The content analysis method was used to analyze the extracted data and the findings were organized in tables and figures to meet the research objectives.
Results: After reviewing 7300 studies, 25 full-text studies were selected for final analysis based on their relevance to the research objectives. The findings showed that AI methods, especially deep learning (DL), are widely used to predict and diagnose hematological and Hematopathological diseases. Among the most common algorithms used in ML were XGBoost, which was one of the most important deep learning algorithms, as well as Convolutional Neural Networks (CNN). AI-based models had Accuracy, Specificity, and Sensitivity of 96.6%, 95%, and 96%, respectively.
Conclusion: This review shows that AI-based models have the potential to be significantly applied to the analysis of blood data. As artificial intelligence continues to evolve, medical professionals and researchers will have access to powerful ML-based tools to quickly and accurately diagnose.
