@article{ 
author = {Jalalian, Davoud and Ashoori, Safour},  
title = {Design an Optimized Deep Learning Model for Liver Disease Prediction}, 
abstract ={Introduction: The incidence of liver disease resulting from excessive alcohol consumption, inhalation of polluted gases, drug use, contaminated food, and food packaging is rapidly increasing. As this disease often presents no symptoms or complications in its early stages, timely diagnosis can be challenging. However, if diagnosed early, treatment can be administered more easily and at a lower cost. In more advanced stages, liver diseases can progress to cirrhosis and liver cancer, leading to significantly more difficult treatment options and, in some cases, resulting in the patient's death. Method: Considering the challenges involved, early diagnosis of liver disease is crucial for determining treatment solutions, duration of treatment, and recovery. With advancements in machine learning and deep learning technologies, which can analyze and learn from complex and large datasets, these tools can be employed for the early prediction of liver disease. In this study, we utilized a dataset of liver disease patients from India. After data preprocessing, a deep learning model optimized by the metaheuristic algorithm of the Lizard Search was proposed to enhance prediction accuracy by leveraging the advantages of metaheuristic algorithms and to assist in timely disease diagnosis. Results: Our proposed method employs a deep neural network optimized using the metaheuristic RSA algorithm. This model achieved promising results in diagnosing liver disease, with an approximate accuracy of 96.9%, a reliability of around 97.2%, and an F1 score close to 96.7%. This approach demonstrates high efficiency in the early and accurate detection of liver diseases, contributing to improved treatment processes. Conclusion: The results demonstrated that combining a feedforward neural network with metaheuristic algorithms significantly improved the accuracy and reliability of liver disease diagnosis. The proposed model enhanced prediction accuracy and diagnostic validity compared to previous methods. These advancements can effectively facilitate the early detection of liver diseases, leading to improved treatment processes and a reduction in complications associated with disease progression.},  
Keywords = {Liver disease, Machine learning, Deep learning, Metaheuristic algorithm, Deep neural network},
volume = {12},
Number = {1}, 
pages = {1-17}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.23},
url = {http://jhbmi.ir/article-1-882-en.html},  
eprint = {http://jhbmi.ir/article-1-882-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Alidadi, Rezvan and SadeghiNaeini, Has},  
title = {Equipment Design for Telemedicine Examinations: An Approach to Enhancing Primary Healthcare}, 
abstract ={Introduction: In many developing countries, particularly Iran, providing medical services in rural areas faces numerous challenges, including shortages of healthcare professionals, dispersed populations, limited infrastructure, and high travel costs. In this context, telemedicine emerges as an innovative solution that can enhance both access to and efficiency of healthcare services. However, there is still limited evidence regarding the feasibility of replacing in-person examinations with remote alternatives. This study aims to offer a comprehensive framework to guide designers in understanding contextual challenges and effectively navigating the early stages of designing telemedicine examination equipment before defining specific technical solutions. Method: This descriptive-analytical study was conducted in two phases. First, needs were identified through field observations and semi-structured interviews with 25 residents of the rural village of Zamenjan. In the second phase, a multidisciplinary team comprising physicians, local health workers, and residents collaboratively developed a checklist for designing remote medical examination tools, ensuring alignment with both community needs and professional medical standards. Results: Qualitative data were analyzed using thematic analysis. Key themes included resource limitations, technological access barriers, and implementation challenges. These insights were directly utilized to develop the final design checklist, highlighting essential features such as a modular structure, a user-friendly interface, a sustainable power supply, data security, and the incorporation of advanced technologies like augmented reality and haptic feedback. Conclusion: The findings indicate that developing remote examination equipment through a comprehensive and multidisciplinary approach can significantly enhance access to and the quality of medical services in rural and underserved areas. By utilizing the provided checklist, designers can create efficient, sustainable, and user-friendly tools that address local limitations and specific needs, ultimately improving access to medical services and the quality of primary healthcare in these regions.},  
Keywords = {Examination Equipment, General Examinations, Product Design, Telemedicine, Rural Areas},
volume = {12},
Number = {1}, 
pages = {18-29}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.08},
url = {http://jhbmi.ir/article-1-905-en.html},  
eprint = {http://jhbmi.ir/article-1-905-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Taghizadeh, Fatemeh and Mohammadi, Alireza and Rahmati, Mansour and Moghabeli, Roy},  
title = {Spatial Distribution Analysis of Hypertension Patients: A Case Study}, 
abstract ={Introduction: Hypertension is a major global public health challenge, closely linked to serious complications such as stroke and heart attack. Spatial analysis, as a modern tool in health geography, provides insights into disease distribution and its relationship with environmental and demographic factors. This study aimed to investigate the spatial distribution of hypertension patients in the Abotaleb neighborhood of Ardabil city and to offer spatially informed recommendations for improving access to healthcare services. Method: This descriptive-analytical study utilized secondary data from medical records of 600 patients diagnosed with hypertension, aged 36 to 87, between 2018 and 2022. Patients’ addresses were geocoded using Google Maps and OpenStreetMap and analyzed in ArcGIS. Analytical techniques included Kernel Density Estimation, Hot Spot Analysis, Shortest Distance to Facilities, Average Nearest Neighbor Index, Global and Local Moran’s I, Buffer Analysis, Service Area Network, and the Operational Radius Method. To reduce geolocation errors, ambiguous or incomplete records were reviewed and removed. A consistent coordinate system was maintained throughout the analysis. Results: The findings revealed that the spatial pattern of hypertension was clustered. Initially, clusters were concentrated in the central and northern parts of the neighborhood but later spread to the southern and western areas. Higher densities were linked to proximity to healthcare centers and population concentration. The disease was more prevalent among women (62.7%), with most cases occurring in the 56–65 age group. Additionally, 68% of patients had at least one comorbid condition, such as diabetes or obesity. The highest number of cases was recorded in 2021, possibly due to enhanced screening and lifestyle changes during the COVID-19 pandemic. These findings align with global reports, including those from the World Health Organization. Conclusion: This study highlights the need for optimized healthcare facility locations, targeted screening for high-risk groups, and integrated interventions in vulnerable areas. Despite limitations related to clinical data and geographic scope, the results provide a valuable foundation for localized health planning and reducing inequalities in healthcare access.  },  
Keywords = {Hypertension, Geographic Information Systems (GIS), Spatial Analysis},
volume = {12},
Number = {1}, 
pages = {30-51}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.09},
url = {http://jhbmi.ir/article-1-906-en.html},  
eprint = {http://jhbmi.ir/article-1-906-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {SedaghatPourHaghighi, Mohammad Mehdi and Kazemi, Farangis and Saeidmanesh, Mohsen and Dolatabadi, Shiva and Demehri, Farangis},  
title = {Evaluating the Effectiveness of Virtual EMDR Therapy Platform on Reducing PTSD Symptoms}, 
abstract ={Introduction: Post-Traumatic Stress Disorder (PTSD) has profound adverse effects on mental health. Eye Movement Desensitization and Reprocessing (EMDR) therapy has been endorsed as an effective intervention for PTSD; however, limited access to this method remains a significant challenge. Given the potential of digital therapeutic innovations to address such barriers, further research is warranted to evaluate the efficacy of online EMDR and its viability as a substitute for in-person treatment. Method: This study employed a sequential exploratory design and a quasi-experimental randomized controlled trial with pre-test, post-test, and three-month follow-up. Forty PTSD patients in Shiraz were screened using structured clinical interviews and randomly assigned to intervention and control groups. The intervention group received eight individual EMDR sessions (60–90 minutes) through a virtual EMDR platform. Data were collected via standardized questionnaires and analyzed in Python using mixed ANOVA, Bonferroni post-hoc tests, and effect size calculations. Results: The findings indicated that participants in the treatment group experienced a statistically significant reduction in PTSD symptom severity compared to the control group. Bonferroni post hoc tests confirmed that the difference between pretest and posttest scores in the treatment group was statistically significant, while no significant difference was observed between posttest and three-month follow-up scores—suggesting the stability of therapeutic outcomes. Moreover, participants in the treatment group reported high levels of satisfaction regarding the quality and usability of the virtual platform. Conclusion: The EMDR-based virtual therapy platform developed in this study demonstrated high efficacy in the treatment of PTSD and offers a promising, innovative alternative to conventional face-to-face therapy. This approach also presents advantages such as cost reduction, enhanced accessibility, and improved therapeutic experience. The results align with similar international findings. Furthermore, theoretical models such as the Adaptive Information Processing (AIP) model and Cognitive Load Theory offer explanatory frameworks for the effectiveness of digital EMDR interventions. Future studies are encouraged to explore the long-term effects of this intervention and its application to other psychological disorders.},  
Keywords = {Internet-Based Intervention, Eye Movement Desensitization and Reprocessing Therapy, Post-Traumatic Stress Disorder, Randomized Controlled Trial},
volume = {12},
Number = {1}, 
pages = {52-63}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.10},
url = {http://jhbmi.ir/article-1-920-en.html},  
eprint = {http://jhbmi.ir/article-1-920-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Kashani, Mostafa and RahmanSetayesh, Shayan and Barzekar, Seddigheh},  
title = {Diagnosis and Analysis of Brain Tumors in Medical Images Using a Feedforward Neural Network Optimized with Quantum-Inspired Algorithms}, 
abstract ={Introduction: Rapid and accurate diagnosis of brain tumors significantly improves treatment planning and survival rates. However, the manual review of multi‑modal magnetic resonance images is often slow and prone to errors due to lesion heterogeneity, similarity to healthy tissue, and large data volumes. This study presents an automated framework that incorporates a lightweight feed‑forward neural network with an intrinsic attention mechanism and quantum‑behaved particle swarm optimization (QPSO). The aim of this study is to improve the speed and accuracy of tumor identification while maintaining interpretability in clinical environments with limited resources. Method: Multimodal MRI images (T1, T1ce, T2, and FLAIR) were sourced from reputable databases, including the Brain Tumor Segmentation Challenge (BraTS) and The Cancer Imaging Archive (TCIA). The images underwent preprocessing, which included intensity normalization (Z-score), noise reduction using Gaussian and median filters, and correction of intensity inhomogeneity. Statistical, textural, and frequency-based features were extracted and reduced to 300 principal components using Principal Component Analysis (PCA). Feature weighting was performed using a document relevance-inspired method. The proposed model, a five-layer feedforward neural network (FNN) with a ReLU activation function and an internal attention mechanism, was optimized using QPSO. Heatmaps were generated to enhance result interpretability. Results: The proposed model achieved an accuracy of 99.6 %, sensitivity of 99.4 %, and specificity of 99.7 %, outperforming reference convolutional networks (97.1 %) and U‑Net architectures (96.2 %). The mean prediction time per image was less than 0.5 seconds, facilitating real‑time clinical use. Heatmaps produced by the attention layer, effectively highlighted abnormal regions and enhanced interpretability. These metrics were consistently replicated across multiple random splits, and qualitative evaluations by imaging specialists confirmed the absence of specificity loss and the clinical relevance of the findings. Conclusion: A feedforward network equipped with intrinsic attention and optimized with QPSO demonstrated near‑perfect accuracy and sub‑second inference for brain tumor diagnosis on multi‑modal MRI. Its high performance on standard GPUs, combined with the generation of intuitive heatmaps, positions this framework as a practical decision‑support tool, particularly in centers lacking advanced infrastructure. Future evaluations will focus on multi‑center data and deployment on edge devices to strengthen clinical adoption and regulatory compliance.},  
Keywords = {Brain Tumor Detection, Feedforward Neural Network, Attention Mechanism, Medical Image Segmentation},
volume = {12},
Number = {1}, 
pages = {64-78}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.11},
url = {http://jhbmi.ir/article-1-922-en.html},  
eprint = {http://jhbmi.ir/article-1-922-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Meraji, Marziyhe and Shariatnia, Sahar and Ameri, Fatemeh},  
title = {Examining the Usability of The SIPAD System Using an Exploratory Evaluation Method}, 
abstract ={Introduction: The SIPAD system is used by a diverse range of users, including students, professors, administrators, and university staff. Therefore, it requires continuous evaluation to prevent possible errors and identify problems in the system, plan for resolving them, and ultimately provide better quality services. Thus, this study was conducted to evaluate the usability of the SIPAD system, aiming to identify and resolve problems within it. Method: This descriptive cross-sectional study was conducted in 2024 to evaluate the usability of the SIPAD system at Mashhad University of Medical Sciences using an exploratory evaluation method. The evaluators independently examined different parts of the SIPAD system (from the perspective of students, professors, and the education unit) using the Pierotti checklist for compliance with the Nielsen usability principles. For each type under study, evaluators were asked to rate the severity of the problem related to the item from 0 to 4 if they selected the “No” option (including non-compliance with the desired item in the SIPAD system). Results: Sixteen evaluators participated in the study. 56.25 % of the evaluators were master's and Ph.D. students, 37.5 % were faculty members in health information management, medical informatics, and artificial intelligence, and 6.25 % were experts in the education unit. The majority of problems identified were related to the principles of website flexibility and efficiency (1.35), guidance and documentation (1.28), and the fewest problems accounted for aesthetic aspects and simple design (0.08). The average severity of the identified problems showed that the SIPAD system has minor problems in some Nielsen's usability principles. Conclusion: This study identified shortcomings in user experience design by evaluating the SIPAD system. The findings show that despite proper technical performance, the neglect of user-centered principles has reduced productivity and satisfaction. The main problems include incomprehensible symbols, a lack of return capabilities, and failure to address the real needs of users. The proposed solutions are low-cost, operational, and have the potential to significantly improve the user experience. Continuous improvement through periodic evaluations and attention to user feedback is essential for the system to remain dynamic.},  
Keywords = {Usability Assessment, Exploratory Assessment, SIPAD System},
volume = {12},
Number = {1}, 
pages = {79-90}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.12},
url = {http://jhbmi.ir/article-1-937-en.html},  
eprint = {http://jhbmi.ir/article-1-937-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Nabizadeh, Amir Hossei},  
title = {Role of Recommender Systems in Optimizing Healthcare and Personalizing Treatment}, 
abstract ={The article is a letter to the editor, so it does not have an abstract.},  
Keywords = {Recommender systems, Health, Treatment, Personalization},
volume = {12},
Number = {1}, 
pages = {91-95}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.13},
url = {http://jhbmi.ir/article-1-927-en.html},  
eprint = {http://jhbmi.ir/article-1-927-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Shafian, Sara and Monemi, Erf},  
title = {The two Sides of the Artificial Intelligence Coin in Medical Sciences: a Look at the Human and Ethical Dimensions}, 
abstract ={Introduction: Artificial Intelligence (AI) has transformative potential in medical sciences, promising increased accuracy in diagnosis, disease progression prediction, and personalized treatments. However, the growing emphasis on technical aspects has raised significant human and ethical challenges related to doctor-patient communication, privacy, accountability, and trust. This study aims to examine these two contrasting aspects—opportunities and challenges—while emphasizing the necessity of integrating artificial intelligence with human values in medicine. Method: This research was conducted as a narrative review by searching scientific databases, including PubMed, Scopus, and Web of Science, using relevant keywords. Articles published between 2014 and 2024 that examined the human, communicative, and ethical aspects of AI applications in medicine were collected and analyzed through qualitative content analysis. Results: The findings revealed that while AI automates repetitive tasks and analyzes vast amounts of data, allowing physicians to focus more on patient care, significant challenges were identified in several areas: deficiencies in establishing empathetic and nonverbal communication, concerns regarding data privacy and security, ambiguity in legal and ethical accountability in the event of errors, and the risk of algorithmic bias, which could lead to inequalities in healthcare delivery. Conclusion: The successful implementation of AI in medicine requires moving beyond a purely technical perspective and adopting a human-centered approach. AI should function as an assistive tool for physicians rather than replace their human judgment and communication. To ensure the future of this collaboration, it is essential to develop transparent ethical frameworks, train healthcare professionals, and design efficient systems that uphold human values.},  
Keywords = {Artificial Intelligence, Medicine, Ethics, Medical Confidentiality, Professional Responsibility},
volume = {12},
Number = {1}, 
pages = {96-104}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.14},
url = {http://jhbmi.ir/article-1-928-en.html},  
eprint = {http://jhbmi.ir/article-1-928-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@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}  
}

@article{ 
author = {Tamkin, Mohammad Reza and Jalalkamali, Hoda and NezamAbadi-pour, Hossei},  
title = {Classification of Schizophrenia Patients using EEG- based Functional Connectivity Map}, 
abstract ={Introduction: Functional brain connectivity derived from electroencephalography (EEG) has been widely used to investigate dynamic cognitive processes, information flow in the brain, and the diagnosis of neurological and psychiatric disorders such as schizophrenia. Schizophrenia is a complex disorder characterized by disruptions in functional and attentional brain networks. Accurately examining these alterations requires analytical approaches with high temporal resolution and network-based modeling capabilities. The aim of this study was to analyze patterns of effective brain connectivity in patients with schizophrenia compared with healthy individuals using EEG data and to apply advanced deep learning models for group classification. Method: This study analyzed EEG-based brain connectivity networks of 17 healthy participants and 19 patients with schizophrenia while they performed a task involving the comparison of unilateral and bilateral visual processing. After data processing, functional brain graphs were constructed, and four graph-theoretical metrics including node strength, global efficiency, clustering coefficient, and modularity index were calculated. These features were then used to classify healthy participants and patients with schizophrenia using a deep learning model based on a Long Short-Term Memory (LSTM). Results: The performance of the classification model was evaluated using accuracy, precision, recall, and F-score metrics. The proposed method achieved an accuracy of 94%, outperforming previous studies that classified patients with schizophrenia and healthy participants based on functional connectivity features. Conclusion: The results demonstrate the effectiveness of an LSTM-based recurrent deep learning model in distinguishing patients with schizophrenia from healthy individuals using EEG data. Additionally, the significance of features obtained from the functional brain graph and the task utilized is emphasized. The high classification accuracy suggests that the observed deficits in attention-related brain networks in patients with schizophrenia particularly during unilateral and bilateral visual processing. These deficits represent reliable neural markers differentiating patients with schizophrenia from healthy individuals.},  
Keywords = {Schizophrenia, Classification, EEG, Functional connectivity},
volume = {12},
Number = {3}, 
pages = {216-235}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.24},
url = {http://jhbmi.ir/article-1-869-en.html},  
eprint = {http://jhbmi.ir/article-1-869-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Kiadeh, Alireza Asghari and Motamed, Sar},  
title = {Predicting Cardiovascular Diseases from EEG Signals Using YOLO Network Composition and XGBoost Algorithm}, 
abstract ={Introduction: Cardiovascular diseases continue to be one of the leading causes of mortality worldwide, making early detection crucial for reducing complications and fatalities. Traditional diagnostic methods often depend on the manual interpretation of cardiac signals, which can be time-consuming and reliant on the expertise of clinicians. However, advances in deep learning and data mining have facilitated the automated analysis of medical signals and the detection of hidden patterns. This study presents a novel approach to identifying cardiac diseases based on ECG signals. Method: In this study, we developed a hybrid deep learning model that utilizes the fast version of the YOLO network to extract mid-level features from ECG signals, combined with the XGBoost algorithm to improve classification performance. ECG signals from the MIT-BIH Arrhythmia Database were first extracted and preprocessed. These signals were then input into the YOLO network to generate feature vectors. The outputs of the network were subsequently fed into the XGBoost algorithm for final classification using an ensemble of multiple weak decision trees. Finally, we compared the proposed model with established conventional methods. Results: The results demonstrated that the YOLO network effectively extracts key features from ECG signals, and its integration with XGBoost significantly enhances overall model accuracy. The proposed model outperformed baseline methods, including simple neural networks and support vector machines (SVMs). Evaluation on the MIT-BIH Arrhythmia Database revealed substantial improvements in classification accuracy, as well as enhanced sensitivity and specificity. These findings indicate that combining deep learning with boosting algorithms provides an efficient approach for medical signal analysis. Conclusion: The YOLO-XGBoost hybrid model offers an accurate and innovative approach for detecting cardiac diseases from ECG signals. In addition to enhancing classification accuracy, this method is well-suited for implementation in clinical decision support systems and can serve as an efficient tool for patient screening. Future work could involve validating the model with larger and more diverse datasets and integrating it into intelligent medical systems.},  
Keywords = {Heart disease, arrhythmia, deep learning, cardiac signals, YOLO, XGBoost.},
volume = {12},
Number = {3}, 
pages = {236-249}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.25},
url = {http://jhbmi.ir/article-1-929-en.html},  
eprint = {http://jhbmi.ir/article-1-929-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Sarafinejad, Afshin and ShojaeiBaghini, Mahdie and Ahmadipour, Alihas},  
title = {Development of an Ontology Model for the Blood and Blood Products Supply and Demand Chain in Iran}, 
abstract ={Introduction: Ontology, as a formal method for accurately representing concepts, entities, and their relationships, plays a vital role in modeling complex processes. Despite numerous studies in this field, the application of ontology-based models in Iran’s blood transfusion chain remains limited. This study aims to develop an ontology model for blood supply and demand in Iran to support decision-making, facilitate information exchange, and enhance efficiency and safety in the blood transfusion chain by integrating relevant concepts and processes. Method: The METHONTOLOGY methodology was used to develop the ontology model, which includes the stages of specification, knowledge acquisition, conceptualization, integration, implementation, evaluation, and documentation. The ontology was implemented in Protégé (version 5.6.3), and its quality was assessed using the FOCA methodology. Additionally, two diagrams, a data flow diagram and a swimlane diagram, were created to enhance the conceptualization of processes. Results: Two ontologies were developed: one for hospitals and another for the Blood Transfusion Organization. The hospital ontology comprised 26 classes and subclasses, 46 object properties, 43 data properties, and 644 axioms. In contrast, the Blood Transfusion Organization ontology included 11 classes and subclasses, 24 object properties, 25 data properties, and 318 axioms. After defining the components and rules, graphical models of both ontologies were created. The quality evaluation results indicated high quality, with scores of 0.995 for the hospital model and 0.951 for the Blood Transfusion Organization model. Conclusion: Designing and developing an ontology model for the blood supply and demand chain enhances the understanding of domain processes, facilitates data sharing, and supports knowledge documentation and transfer to users. Effective ontology development requires a comprehensive understanding of the activities, tasks, concepts, and relationships within the target domain. Furthermore, ontology evaluation is crucial for identifying conceptual deficiencies, optimizing knowledge structures, and improving organizational processes.},  
Keywords = {Ontology, Blood transfusion, blood supply, Hemovigilance},
volume = {12},
Number = {3}, 
pages = {250-264}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.26},
url = {http://jhbmi.ir/article-1-951-en.html},  
eprint = {http://jhbmi.ir/article-1-951-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Deldar, Kolsoum and Tavousi, Seyed Hasan and Khodabandeloo, Farnoosh and Rakhsha, Mohsen and Froutan, Razieh},  
title = {Exploring the Experiences of Elderly Burn Victims with Kinect-Based Motor Rehabilitation: A Qualitative Study}, 
abstract ={Introduction: Elderly burn victims face physical limitations that hinder rehabilitation. Technologies like Kinect motion systems have been used to boost patient participation; however little is known about their experiences and perceptions. This study explores elderly burn patients’ experiences with Kinect-based motor rehabilitation. Method: This qualitative study was conducted in the spring of 2025 using a conventional content analysis approach. Participants were purposively selected from hospitalized older adults with burn injuries admitted to the Burn Unit of Imam Reza Hospital in Mashhad, Iran, all of whom had experience participating in a Kinect‑based technology‑assisted rehabilitation program. Data were collected through semi‑structured interviews. Following transcription, the data were managed in Microsoft Excel (version 2019) and systematically coded and categorized. Results: Analysis of 18 individual interviews and one focus group discussion yielded 215 initial codes, organized into four main categories and 12 subcategories. The four themes were: "from confusion to adaptation", "enhanced motivation and engagement in the treatment process", "perceived independence and self- efficacy", and "sense of safety and comfort with technology". Findings revealed that while the use of technology in rehabilitation was initially associated with feelings of unfamiliarity and anxiety, adaptation improved over time through increased selfconfidence and support from healthcare professionals and family members. Technology enhanced patients’ motivation, active participation, and adherence to rehabilitation exercises, making the process more enjoyable. Additionally, increased independence, improved selfefficacy, and reduced reliance on others emerged as key outcomes. Ultimately, the technology fostered a sense of safety and control, reduced stress and pain during exercises, and improved the overall rehabilitation experience. Conclusion: Kinect-based rehabilitation can enhance elderly burn victims’ participation, independence, and satisfaction; however, individual and contextual challenges must be addressed for effective use in this population.},  
Keywords = {Rehabilitation, Kinect, Elderly, Burns, Qualitative Study, Lived Experience},
volume = {12},
Number = {3}, 
pages = {265-276}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.27},
url = {http://jhbmi.ir/article-1-958-en.html},  
eprint = {http://jhbmi.ir/article-1-958-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Falahatipour, Sakineh Khanamani and Soltani, Motahareh and Karami-Mohajeri, Somayyeh and Ghasemloo, Amirreza and KhanamaniFalahati-pour, Soudeh and KhanamaniFalahatipour, Salimeh and Oghabian, Zohreh},  
title = {A Narrative Critical Review of AI Innovations in Toxicity Prediction and Personalized Healthcare}, 
abstract ={Introduction: Predictive toxicology utilizing artificial intelligence (AI) enables rapid, accurate, and cost-effective assessment of the toxicity of chemicals, pharmaceuticals, and environmental pollutants. By reducing reliance on traditional in vitro and animal testing, this field plays a critical role in drug development, personalized medicine, and environmental health. This study reviews recent advances in AI applications in predictive toxicology and examines the associated opportunities and challenges. Method: This narrative critical review was conducted through a systematic search of scientific databases, including PubMed, Scopus, ScienceDirect, and Google Scholar. Keywords such as "Artificial Intelligence", "Predictive Toxicology," and "Personalized Medicine" were used to identify relevant articles. Articles were selected based on inclusion criteria (focusing on machine learning methods, published from 2000 onward, and full-text availability) and exclusion criteria (irrelevant articles or those lacking experimental data), and were critically evaluated. Results: Machine learning and deep learning algorithms have enhanced predictive models such as QSAR, toxicogenomics, and molecular modeling. These technologies have improved the accuracy of toxicity predictions, accelerated compound screening, and reduced the need for animal testing. In personalized medicine, AI predicts drug toxicity and optimizes therapeutic dosages by analyzing genetic profiles. In environmental health, wearable sensor data are analyzed to monitor the pollution effects. However, challenges include limited high-quality data, restricted model interpretability, and regulatory barriers. Conclusion: Artificial intelligence has significant potential to transform predictive toxicology and enhance human and environmental safety. To fully harness this technology, data standardization, the development of explainable AI models, and the establishment of effective regulatory frameworks are essential. Future directions include real-time toxicity monitoring and the integration of AI with emerging technologies such as CRISPR. These approaches can lead to more informed decision-making, reduced uncertainty in risk assessment, and an accelerated transition toward preventive and personalized toxicology.},  
Keywords = {Artificial Intelligence, Toxicity Prediction, Personalized Medicine, Machine Learning, Toxicogenomics, Molecular Modeling},
volume = {12},
Number = {3}, 
pages = {277-293}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.28},
url = {http://jhbmi.ir/article-1-935-en.html},  
eprint = {http://jhbmi.ir/article-1-935-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Beigzadeh, Amin and Montazeri, Mahdieh},  
title = {Telemedicine-Based Clinical Rounds: A Strategy for Enhancing Patient Safety in Teaching Hospitals}, 
abstract ={The article is a letter to the editor, so it does not have an abstract.},  
Keywords = {Medical Education, Telemedicine, Health Informatics, Clinical Rounds},
volume = {12},
Number = {3}, 
pages = {294-297}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.29},
url = {http://jhbmi.ir/article-1-962-en.html},  
eprint = {http://jhbmi.ir/article-1-962-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Shoeibi, Nasser and HashemiHasani, Mojtaba and Shirdel, Arash and Farrahi, Razieh},  
title = {User-Centered Requirements for an Electronic Ophthalmology Record: A Mixed-Methods Study}, 
abstract ={Introduction: The unique characteristics of clinical workflows and ophthalmic data management processes necessitate a distinct design for ophthalmic electronic medical record (EMR) systems compared to those developed for other medical specialties. This study aimed to identify the functional requirements of an ophthalmic EMR system specifically tailored to the operational needs of ophthalmology hospitals and specialized eye-care clinics. Method: This descriptive cross-sectional study was conducted in 2019 at Khatam Al-Anbia Eye Hospital in Mashhad, Iran. Functional requirements for the ophthalmic EMR system were identified using a multimethod approach, which included brainstorming sessions, field surveys, literature reviews, semi-structured interviews, focus group discussions, and the Delphi method. Results: The identified functional requirements were categorized into four main domains: clinical documentation, physician order management, clinical results management, and reporting. Within the clinical documentation domain, the most important requirements were “creating electronic records for patients” (mean: 4.93) and “scanning and displaying previous outpatient records” (mean: 4.79). For physician order management, the highest-rated requirements were “scheduling surgical procedures within the system” and “tracking consultations and referrals” (mean: 4.53). In the clinical results management domain, the most important requirements included “recording laboratory test results in the patient record and enabling physician access to these results” and “capturing and storing special disease cases to establish ophthalmic disease registries”(mean: 4.67).Overall, clinical results management received the highest mean score (4.56), followed by clinical documentation (4.47), reporting (4.40), and physician order management (4.38), indicating the highest and lowest priorities among the identified functional requirements. Conclusion: Clinical results management has emerged as the highest-priority functionality for ophthalmic EMR systems. Ophthalmology is a high-volume specialty characterized by complex workflows and team-based patient care models. Timely diagnosis and effective treatment of ocular diseases heavily rely on a wide range of diagnostic and paraclinical investigations. Consequently, immediate access to clinical results is essential for enhancing the quality of care, enabling timely diagnosis, informed clinical decision-making, and early therapeutic interventions.  },  
Keywords = {Electronic health record, Health information system, Functional requirement, Eye diseases, Ophthalmology},
volume = {12},
Number = {4}, 
pages = {298-314}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.01},
url = {http://jhbmi.ir/article-1-970-en.html},  
eprint = {http://jhbmi.ir/article-1-970-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {AhmadiRad, Zahra and GhayoumiZadeh, Hossein and Fayazi, Ali and Rezaee, Khosro},  
title = {Integration of Image and Tabular Data in a Siamese Network for Diabetic Patient Classification Using Thermography: A Multimodal EfficientNetB3-Based Approach}, 
abstract ={Introduction: Diabetic foot is a serious chronic complication of diabetes that can lead to ulcers and limb amputation if not diagnosed early. while non-invasive techniques like thermography can prevent severe complications, traditional methods often fail to identify subtle thermal changes.  This study developed an intelligent system for diabetic foot detection using thermographic images and numerical features. Method: The Plantar Thermogram Database, containing 167 samples, was divided into training (116 samples), validation (25 samples), and test (26 samples) sets. A multimodal Siamese architecture based on EfficientNetB3 processed left and right foot thermograms simultaneously.  Outputs were combined using ∣L−R∣ and L⊙R to model thermal asymmetry, then fused with normalized tabular features. Implementation used TensorFlow and Keras, with class weighting to address data imbalance. Results: The model achieved 96.15% accuracy, 95% sensitivity, 100% specificity, 100% precision, 85.71% NPV, 97.44% F1-score, and 97.5% balanced accuracy on the test set.  Learning curves showed rapid convergence with no overfitting and stable performance. Conclusion: The proposed Siamese architecture, utilizing both image and tabular data, offers a non-invasive, rapid, and accurate approach for screening and monitoring diabetic patients, potentially facilitating early diagnostic clinical systems for diabetic foot detection.},  
Keywords = {Diabetic foot, Thermography, Siamese architecture, Multimodal data, Early detection, EfficientNetB3},
volume = {12},
Number = {4}, 
pages = {315-333}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.02},
url = {http://jhbmi.ir/article-1-953-en.html},  
eprint = {http://jhbmi.ir/article-1-953-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Ayeneh, Masoumeh and Hosseinpour, Mahdi and Veisi, Korush},  
title = {Presenting a Modern Entrepreneurship Model with an Approach to Wearable Technologies and Digital Health in Kermanshah province}, 
abstract ={Introduction: At the heart of the digital revolution, a profound transformation is taking place, erasing the boundaries between technology, health, and daily life. Here lies the new realm of entrepreneurship, where the goal is not merely to build startups, but to create intelligent ecosystems that are deeply intertwined with human health. The driving force behind this shift is the emergence of wearable technology and digital health. Today, devices like smartwatches are no longer just for telling time; they have become guardians of our health, monitoring heart rate, sleep patterns, and stress levels. Method: At the heart of the digital revolution, a profound transformation is taking place, erasing the boundaries between technology, health, and daily life. Here lies the new realm of entrepreneurship, where the goal is not merely to build startups, but to create intelligent ecosystems that are deeply intertwined with human health. The driving force behind this shift is the emergence of wearable technology and digital health. Today, devices like smartwatches are no longer just for telling time; they have become guardians of our health, monitoring heart rate, sleep patterns, and stress levels. Results: The causal factors were identified as technological, environmental & market-related, organizational & entrepreneurial, and socio-cultural factors. Intervening conditions included legal & regulatory, economic & financial, and technical & infrastructural factors. Contextual conditions encompassed geographical & climatic, systemic & structural, knowledge-based & research, and industrial & manufacturing factors. The strategies consisted of technological & innovation strategies, commercial & market-oriented strategies, internationalization strategies, financial & investment strategies, and legal & regulatory strategies. The outcomes were categorized into three main types: the creation of new economic value, socio-behavioral transformations, and emergent & forward-looking factors. Conclusion: This research demonstrated that the integration of these technologies not only enables the provision of personalized healthcare solutions but also creates a novel framework for entrepreneurial business models. By leveraging the capabilities of real-time data analytics, artificial intelligence, and the Internet of Things (IoT), startups can respond to the growing market demands in the fields of disease prevention, diagnosis, and management.},  
Keywords = {Entrepreneurship, Wearable Technology, Digital Health, Grounded Theory, Innovation},
volume = {12},
Number = {4}, 
pages = {334-347}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.03},
url = {http://jhbmi.ir/article-1-963-en.html},  
eprint = {http://jhbmi.ir/article-1-963-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Hoseinnia, Elahe and Zarrabi, Mahboobeh and Hajimahdi, Zahr},  
title = {Multiscale Molecular Dynamics Simulation of Lipid Bilayer Remodeling in Cancer: Biophysical Effects of the Transition from Normal to Cancerous States in Breast Cell Membranes}, 
abstract ={Introduction: Cancer cell membranes exhibit altered lipid composition and asymmetry, influencing tumor progression and drug resistance. These lipidomic changes can significantly influence membrane structure, biophysical properties, and permeability. This study aims to investigate the biophysical consequences of lipidomic changes in breast cell membranes during cancer development. Method: Normal and cancer breast membrane models were built using reported lipidomic compositions. The normal model had an asymmetric lipid distribution, while the cancer model was symmetric, with increased phosphatidylserine (PS) and phosphatidylethanolamine (PE) in the outer leaflet. Molecular dynamics simulations were conducted at all-atom (CHARMM36m,200 ns) and coarse-grained (Martini 2.2,5 µs) resolutions. Structural and dynamical parameters, including bilayer thickness, area per lipid, acyl chain interdigitation, order parameter, and lateral diffusion coefficient, were calculated and compared. Results: The cancer membrane showed increased thickness, reduced area per lipid, higher acyl chain order parameter, and lower lateral diffusion compared to the normal membrane, consistent with reduced chain interdigitation. Conclusion: Changes in cancer cell profiles lead to a more compact, rigid membrane with decreased lateral mobility. These physicochemical alterations may impact therapeutic permeability, suggesting that targeting the biophysical properties of cancer cell membranes could be a promising strategy for novel treatment.  },  
Keywords = {All-Atom molecular dynamics, Cancer, Coarse-Grained simulation, Lipid asymmetry, Membrane},
volume = {12},
Number = {4}, 
pages = {348-358}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.04},
url = {http://jhbmi.ir/article-1-980-en.html},  
eprint = {http://jhbmi.ir/article-1-980-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Hosseini, Seyed Younes and Mohseni, Mahnaz and Dastani, Meisam},  
title = {Analysis of Scientific Production in Telemedicine in Iran: A Literature Review Using Text Mining Approach}, 
abstract ={Introduction: Advances in information and communication technologies have transformed healthcare delivery in recent years, positioning telemedicine as an effective approach to improving accessibility, reducing costs, and supporting continuous patient care. While researchers in Iran have shown increased interest in this field, policy, infrastructural, and cultural barriers persist. This study aimed to identify the trends and topics of telemedicine- related scientific production in Iran. Method: This literature review utilized text mining techniques. Data consisted of 267 scientific records by Iranian researchers, related to telemedicine, retrieved from the Scopus database on September 1, 2025. After preprocessing—including the removal of symbols, stop words, and other irrelevant elements—the data were analyzed. The Latent Dirichlet Allocation (LDA) algorithm was then used to identify the topics of these scientific productions. Results were visualized through word clouds, thematic clusters, and temporal trends. Results: The analysis revealed nine main thematic clusters. The largest share of publications was dedicated to “evaluation and usability of mobile health systems and applications.” Other clusters included policymaking and standardization, mobile health interventions, disease management and self-care, data security and privacy, and methodological development. Conclusion: The findings show that telemedicine-related scientific productions in Iran have grown significantly in recent years, shifting from a sole focus on clinical applications toward broader policy, technological, and managerial dimensions. Nonetheless, limited attention to areas such as data security, cultural acceptance, and equity in access highlights critical gaps that future studies should address.},  
Keywords = {Telemedicine, mHealth, Text Mining, Topic Modeling, Iran},
volume = {12},
Number = {4}, 
pages = {359-371}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.05},
url = {http://jhbmi.ir/article-1-960-en.html},  
eprint = {http://jhbmi.ir/article-1-960-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Shafiei, Elaheh and Madadizadeh, Farz},  
title = {The Necessity of Integrating Explainable Artificial Intelligence into Clinical Decision Support Systems in Primary Health Care}, 
abstract ={},  
Keywords = {Explainable AI, Clinical Decision Support Systems, Primary Health Care, PHC},
volume = {12},
Number = {4}, 
pages = {372-375}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.06},
url = {http://jhbmi.ir/article-1-973-en.html},  
eprint = {http://jhbmi.ir/article-1-973-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Ameri, Arefeh and Salmanizadeh, Farz},  
title = {Multimodal Artificial Intelligence in Medicine: From Integration of Heterogeneous Data to Precision Clinical Decision‑Making}, 
abstract ={The article is a Letter to the Editor; therefore, it does not include an abstract.},  
Keywords = {Multimodal Artificial Intelligence, Heterogeneous Medical Data, Precision Clinical Decision-Making},
volume = {12},
Number = {4}, 
pages = {376-379}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.07},
url = {http://jhbmi.ir/article-1-984-en.html},  
eprint = {http://jhbmi.ir/article-1-984-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}