@article{ author = {Beshratania, Ahmadreza and Ghasemi, Sorayy}, title = {The Role of Artificial Intelligence and Advanced Informatics in the Evolution of Modern Genetics Education}, abstract ={Introduction: With the expansion of next-generation sequencing (NGS) technologies and omics data analysis, genetics education has entered a new phase characterized by large volumes of complex data. In this context, traditional teaching methods have become less effective. Utilizing artificial intelligence (AI) and bioinformatics offers an innovative approach to elevate genetics education to an interactive, data-driven, and analysis-focused level. This study responds to the growing demand for data-driven and analytical training in genetics. Given the vast amount of genomic data and the complexity of the required analyses, employing AI and bioinformatics tools can significantly enhance the quality of education and research in this field. The aim of this study is to investigate the impactful role of advanced AI and bioinformatics in improving modern genetics education. Method: This study was conducted as a narrative review. Scientific sources published in PubMed, Scopus, Web of Science, and Google Scholar between 2005 and 2025 were reviewed. Articles related to the use of AI and informatics in genetics education were selected and analyzed using content analysis. Results: The review results indicated that AI-based tools, including machine learning algorithms, genomic language models, and adaptive training systems, significantly contribute to personalizing education, simulating biological processes, and analyzing genetic variants. Furthermore, practical training in bioinformatics skills—such as working with genetic databases, analytical software, biological programming, and applied biostatistics—empowers students to analyze complex genomic data. However, the lack of digital educational resources and specialized instructors continues to pose a major challenge in data-driven education. Conclusion: The integration of AI and bioinformatics into genetics education offers an innovative approach to training specialists in modern genetics. Developing localized content, virtual training courses, and policies that align the education system with technological advancements are effective strategies for enhancing the quality of genetics education in Iran and similar countries.}, Keywords = {Modern Genetics, Artificial Intelligence, Learning, Data-Driven Learning, Omics Informatics, Deep Learning}, volume = {12}, Number = {2}, pages = {105-116}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2025.15}, url = {http://jhbmi.ir/article-1-939-en.html}, eprint = {http://jhbmi.ir/article-1-939-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2025} } @article{ author = {EslamiJahromi, Maryam and Ayatollahi, Haleh and Ebrazeh, Ali}, title = {Strengthening National Health Hotlines and Call Centers for Crisis Management: A Policy Brief}, abstract ={The COVID-19 pandemic placed significant pressure on Iran’s health system and increased public demand for reliable information and psychosocial support. In response, the 4030 call center provided 24/7 guidance, psychological counseling, and health information to the public. This policy brief is based on a qualitative study conducted in 2023, which involved interviews with 64 participants, including call center staff, consultants, and callers. Data were analyzed using framework analysis to identify key themes related to the call center's performance. The results indicated major strengths, such as large-scale telephone screening, timely psychological support, credible information dissemination, and broad accessibility. However, several challenges were identified, including workforce shortages, training gaps, technological limitations, insufficient integration with the formal health system, and a lack of structured feedback mechanisms. Recommended strategic actions include strengthening technological infrastructure, institutionalizing training, evaluating performance, expanding the service scope, establishing systematic feedback, and stabilizing the workforce. These measures aim to enhance the quality, equity, and resilience of national health hotlines and call centers during future health crises.}, Keywords = {Call center, COVID-19, Hotline, Policy Brief}, volume = {12}, Number = {2}, pages = {117-123}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2025.16}, url = {http://jhbmi.ir/article-1-957-en.html}, eprint = {http://jhbmi.ir/article-1-957-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2025} } @article{ author = {Arab‐Bafrani, Zahra and Nikoubin‐Boroujeni, Majid and Mousavi, Elham}, title = {Identification of Key Oligonucleotide Fragments in the Hepatitis C Virus Genome in Evasion of Interferon Mechanisms Using Weighting Algorithms}, abstract ={Introduction: The presence of short oligonucleotide fragments that constitute the viral genome is unique, and the variation in their abundance among different species significantly influences the pathogenicity of viral strains, evasion of the immune system, and response to antiviral treatment. Data mining and the use of weighting models are effective methods for identifying these characteristic fragments. Given the importance of oligonucleotide fragments in the genomic sequence of the hepatitis C virus (HCV) concerning resistance to interferon treatment and evasion of the host's innate immune system, this study aims to identify these characteristic fragments using weighting models applied to the HCV genomic sequence. Method: In this study, we employed ten different weighting models for the first time to identify short oligonucleotide fragments, including distinguishing dinucleotide, trinucleotide, and tetranucleotide sequences, between two groups of individuals infected with HCV who exhibit different therapeutic responses to interferon (INF) treatment. Ten weighting algorithms were executed and evaluated based on the relative abundance of short oligonucleotide fragments constituting the HCV genome in genotypes 1a, 1b, and 2b from two groups of patients resistant and sensitive to interferon treatment using Rapid Miner software. Results: Several oligonucleotide fragments, including UU, UA, and UC among dinucleotide sequences, and GUA, CUA, and CGUA among trinucleotide and tetranucleotide sequences, showed significant differences between the two groups. Previous studies indicate that the presence of these sequences plays a crucial role in the interferon response against viruses. Conclusion: The results of this study suggest that employing various weighting models in data mining can help predict important genomic indicators of the virus that are effective in evading the host's immune system and antiviral treatments at early stages. These indicators can subsequently be targeted in experimental studies for drug and vaccine design.}, Keywords = {Weighting algorithms, Data mining, Genome sequences, Oligonucleotide fragments}, volume = {12}, Number = {2}, pages = {124-131}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2025.17}, url = {http://jhbmi.ir/article-1-938-en.html}, eprint = {http://jhbmi.ir/article-1-938-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2025} } @article{ author = {Sookhtsaraei, Reza and Sakhaei-nia, Mehdi and Parand, Fereshteh-Azadi}, title = {Hierarchical architectures and service analysis in fog-based healthcare systems: A systematic study}, abstract ={Introduction: Introduction: Healthcare systems are increasingly reliant on computing models to improve the accessibility and quality of services. While cloud computing provides scalability, its inherent limitations—such as latency and bandwidth—negatively impact critical applications. Fog computing emerges as a transformative alternative, bringing processing and storage closer to the network edge. This systematic review evaluates fog computing within the context of healthcare services, addressing service classification, architectural requirements, and deployment challenges. Method: We identified articles between 2018 and 2022 by searching six electronic databases, using keywords related to “Fog Computing” and “Healthcare.” The inclusion criteria were full-text availability and relevance to the topic. Reviews, books, and unrelated studies were excluded. Ultimately, the included articles were analyzed to identify services, assess quality, examine architectures, and evaluate components along with their features, functional, and non-functional requirements. Results: The search generated 3,519 studies, of which 125 studies were included. This study highlights the significance of computing models in healthcare systems based on two main factors: patient mobility and their distance from medical facilities, which necessitate mobile-remote services. These services typically involve remote patient monitoring, wherein vital signs are transmitted to healthcare centers for timely decision-making by care providers. Core functional requirements focus on data manipulation, including sharing and processing, while the main non-functional requirement is performance, with sensitivity to delay. Common architectural models include n-tier and layered structures, where each tier can be deployed independently based on specific needs. Key deployment challenges include limited resources at edge tiers and inadequate dynamic coordination among services. Conclusion: Fog computing enhances healthcare services by reducing the limitations of cloud computing; however, challenges such as standardization and dynamic resource management persist. Future studies should focus on developing service-specific architectures, adaptive deployment strategies for mobile users, and precise event logging. This review highlights the necessity for customizable and verifiable solutions to provide real-time healthcare services, proposing context-aware architectures as a priority.  }, Keywords = {Fog computing, Healthcare services, Quality of services, Functional requirements, Non-functional requirements}, volume = {12}, Number = {2}, pages = {132-158}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2025.18}, url = {http://jhbmi.ir/article-1-933-en.html}, eprint = {http://jhbmi.ir/article-1-933-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2025} } @article{ author = {Motaghi, Najmeh and Arvan, Hamide and ZiaadiniDashtkhaki, Bentolhoda and Shafiei, Kaveh and Azizian, Mahdieh and Kamali, Hoda and Khasseh, Ali Akbar and Khazaneha, Madiyeh and Sharifzadeh, Mahdieh}, title = {Bibliometric and Data Mining Analysis of Research Trends in Multiple Sclerosis with a Focus on Complementary and Alternative Medicine (CAM)}, abstract ={Introduction: Multiple sclerosis (MS) is a multifactorial autoimmune disease that has garnered increasing attention for the use and efficacy of complementary and alternative medicine (CAM) in its management. Due to its complex and chronic nature, MS requires a comprehensive and multidimensional therapeutic approach in which CAM may play a supportive role in improving patients’ quality of life. Method: This study employs bibliometric and data mining methods to examine research trends, key topics, and conceptual developments in MS-related CAM studies. It aims to map the thematic structure, keyword evolution, and scholarly progress in this interdisciplinary field. Data were extracted from the Web of Science database and analyzed using advanced tools such as SciMAT and Biblioshiny. Scientific mapping, thematic clustering, and trend analysis were conducted across two time periods: 2011–2015 and 2016–2020. Hierarchical clustering and keyword dendrograms were utilized to identify core research themes. Results: The findings identified key areas of research, including herbal medicine, dietary interventions, yoga, mindfulness, saturated fatty acids, interleukin-4, evening primrose oil, breathing exercises, and cannabinoids. A significant number of MS patients report using CAM therapies alongside conventional treatments, citing notable benefits. Hierarchical clustering revealed three major thematic clusters: saturated fatty acids, inflammation and CAM, and rehabilitative therapies. Moreover, the adoption of CAM therapies and their impact on patient care have shown a growing trend, while emerging topics such as phytoestrogens and oligodendrocytes gained prominence. Conclusion: Bibliometric analysis in the fields of MS and CAM enhances the current understanding of therapeutic practices and paves the way for future clinical and academic research. This study highlights the importance of integrative, multidisciplinary, and patient-centered approaches in healthcare, guiding researchers toward emerging topics and more effective treatment strategies for MS patients.}, Keywords = {Multiple Sclerosis, Inflammation, Autoimmunity, Bibliometrics, Scientific Mapping}, volume = {12}, Number = {2}, pages = {159-177}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2025.19}, url = {http://jhbmi.ir/article-1-932-en.html}, eprint = {http://jhbmi.ir/article-1-932-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2025} } @article{ author = {Javadian, Reza and Moulaei, Khadijeh and MahmoudzadehSaqeb, Zahra and Dinari, Fatemeh}, title = {Electronic Human Resource Management in Healthcare Organizations: A Cross-Sectional Study}, abstract ={Introduction: Electronic management of employees in health organizations faces several challenges, including financial resource management, electronic recruitment, online training, performance appraisal, electronic reward system, and digital communications. These problems can lead to a decrease in the efficiency and quality of health services. Therefore, the aim of the present study is to investigate how electronic management of employees in health organizations is carried out. Method: This descriptive cross-sectional study was conducted from 2024 to 2025. The research population consisted of 109 health workers at Razi Hospital in Ilam. Sampling was conducted using an easy access method. Data were collected using a standard electronic human resource management questionnaire. Data analysis involved descriptive statistics. Results: The findings revealed that "reducing training costs" (4.07 ± 0.90), "optimal recruitment of qualified candidates" (3.60 ± 0.83), and "providing accurate information to employees through electronic leave programs" (3.54 ± 0.91) obtained the highest average scores. In contrast, the lowest average scores were associated with "sending circulars, work methods, work reports, similar documents electronically" (2.19 ± 0.93), "providing electronic training through educational programs and videos" (2.90 ± 0.93), and "easy access to employment exams electronically" (2.91 ± 1.07). The superior ratings in educational and employment matters suggest that digital-based mechanisms for informing, training, and evaluating employees are crucial. Conclusion: This study can assist researchers and managers in the field of electronic HRM in health organizations by offering solutions to improve these processes. It can also guide the design and implementation of data-driven strategies for monitoring and continuous improvement of employee performance. It is recommended that data security, ongoing user training, and regular collection of feedback from employees be incorporated into the implementation of these systems to enhance efficiency and organizational acceptance.}, Keywords = {Electronic Human Resource Management, E-HRM, Healthcare organizations, E-recruitment, E-learning}, volume = {12}, Number = {2}, pages = {178-188}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2025.20}, url = {http://jhbmi.ir/article-1-940-en.html}, eprint = {http://jhbmi.ir/article-1-940-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2025} } @article{ author = {Khaleghizadeh, Raheleh and Motamed, Sara and Askari, Elham}, title = {Planter Deformity Detection Using Improved Deep Learning Model}, abstract ={Introduction: Flatfoot and other structural deformities of the foot are major causes of musculoskeletal disorders that can significantly impair quality of life. Early detection of these abnormalities is crucial for preventing the progression of complications and selecting appropriate treatment strategies. In recent years, the application of deep learning methods in biomechanics and medical informatics has gained significant momentum, providing powerful tools for the automated and accurate analysis of medical imaging data.  This study aims to develop a novel deep learning-based model for detecting foot abnormalities, addressing structural, angular, and plantar pressure aspects simultaneously. Method: Foot images from both healthy subjects and individuals with abnormalities were collected and preprocessed. Initially, the arch region of the foot was segmented using advanced segmentation techniques. The segmented images were then passed to an enhanced YOLO architecture integrated with the Convolutional Block Attention Module (CBAM), enabling the network to focus more effectively on critical regions. Additionally, angular measurements of the foot were extracted and combined with plantar pressure distribution data to achieve a more comprehensive assessment of foot abnormalities. Results: Experimental evaluations demonstrated that the proposed model achieved an accuracy of 95.14% in detecting foot abnormalities. Comparative analyses with other state-of-the-art methods revealed that the developed approach outperformed competing techniques, not only in classification accuracy but also in computational efficiency and its ability to focus on clinically relevant regions of the foot. The integration of angular and pressure-related features with segmented image data significantly enhanced the system's robustness and precision in identifying various types of abnormalities. Conclusion: The proposed model, with its relatively simple yet effective architecture, provides a reliable solution for the accurate identification of foot abnormalities. This approach can be applied in medical screening, orthopedic insole design, and patient monitoring during rehabilitation. Furthermore, given its computational efficiency and ease of deployment, the model can be integrated into clinical environments and rehabilitation centers. Overall, this research contributes to the advancement of intelligent systems in digital health and predictive medicine.}, Keywords = {Plantar deformity, Flatfoot pressure, Deep learning, YOLO model, CBAM attention mechanism}, volume = {12}, Number = {2}, pages = {189-203}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2025.21}, url = {http://jhbmi.ir/article-1-934-en.html}, eprint = {http://jhbmi.ir/article-1-934-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2025} } @article{ author = {Karami, Mahtab and Afkhami, Shokoofeh and Ghelmani, Yaser and SadeqiJabali, Monireh}, title = {Insurance Deductions from Clinical Documentation Gaps: A Data Quality Perspective}, abstract ={Introduction: A significant portion of hospitals' financial resources is acquired through contracts with insurance companies. Insurance companies often reject claims or impose deductions on hospitals for various reasons, with poor medical record documentation being a major factor. This study aimed to identify the causes and extent of social security insurance deductions while evaluating which dimensions of data quality in medical records were compromised due to inadequate documentation. Method: This descriptive cross-sectional study was conducted in 2024 at Shahid Sadoughi Hospital in Yazd, divided into two phases. In the first phase, data collection was carried out using a checklist to extract services subject to deductions from 893 medical records covered by social security insurance. After grouping these services, the causes and extent of deductions were identified for each service group. In the second phase, documentation-related deductions were categorized according to data quality dimensions. Data analysis was performed using Excel and SPSS, employing descriptive statistics. Results: The highest deduction rate was observed in the emergency department at 24.1%, while the intensive care units had total deductions amounting to 2,980,112,989 rials. By service group, the greatest deductions were related to nursing care services (22.9%) and diagnostic tests (18%). Overall, documentation deficiencies represented the largest proportion of deductions at 45.3%. Within the data quality dimensions, incomplete documentation was the most frequent cause (62.4%). Conclusion: Documentation deficiencies, particularly regarding data completeness, significantly contribute to hospital deductions. Health information systems can help by offering a standardized format for electronic data recording and alerts for essential information. Additionally, it is recommended that physicians and other healthcare providers receive training on documentation practices that align with insurance payment guidelines. Furthermore, regular audits and feedback should be implemented to ensure continuous improvement and maintain the quality of medical record data.}, Keywords = {Hospital, Documentation, Social Security, Insurance, Data Accuracy}, volume = {12}, Number = {2}, pages = {204-215}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2025.22}, url = {http://jhbmi.ir/article-1-947-en.html}, eprint = {http://jhbmi.ir/article-1-947-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2025} }