@article{ 
author = {Rabiee-Ghahfarrokhi, Behzad and BabadiAkashe, Zahr},  
title = {Predicting Depression Using Data Mining Techniques and Artificial Intelligence Algorithms}, 
abstract ={Introduction: Modern lifestyles have led to various mental health disorders and significant psychological issues—such as anxiety, stress, and depression—in many individuals. Psychologists encounter challenges during the interview process that stem from the process itself, the interviewee, and the interviewer; these errors can be attributed to the intricate and highly complex network of interactions occurring during face-to-face exchanges between the interviewer and the interviewee. Given that human beings and human interactions are inherently prone to error, and that the diagnosis of disorders is often influenced by the emotions and feelings of both the client and the therapist, there is a critical need for a tool capable of predicting depression with high accuracy and without human intervention. Method: In this study, real data was used to predict depression and demonstrate the proposed method. The dataset comprised 600 individuals, of whom 297 were diagnosed as not having depression and 303 as having depression. The C5.0 decision tree was employed to extract rules, while a genetic algorithm was used to select the optimal rules yielding the highest prediction accuracy. The proposed method was evaluated based on the weighted count of correctly predicted samples. Results: The results demonstrated that the proposed method offers significantly higher accuracy than many existing machine learning techniques. Furthermore, among all the extracted rules, those exhibiting the greatest impact—along with minimal overlap and conflict—regarding the detection of the presence or absence of depression were selected. Additionally, the algorithm identified the most influential features associated with depression. Based on the analyzed data, suicidal ideation, hypochondriasis, and childhood trauma were identified, in that order, as three significant and influential factors in depression. Conclusion: The use of data mining techniques and machine learning tools is highly efficient and effective for predicting depression. Extracting the most significant features influencing the output variable can substantially reduce time and costs for clients while enhancing the accuracy of the therapist's diagnosis.},  
Keywords = {Data Mining, Depression, Artificial Intelligence, Genetic Algorithm, Decision Tree},
volume = {13},
Number = {1}, 
pages = {1-11}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.08},
url = {http://jhbmi.ir/article-1-915-en.html},  
eprint = {http://jhbmi.ir/article-1-915-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Valizadeh, Nazila and BoloukiSpeily, Omid Reza and Ghorbanzadeh, Parviz},  
title = {An Intelligent Framework Based on Health Informatics for Diagnosing Liver Diseases Using Generative Adversarial Network-Based Data Augmentation and Ensemble Learning}, 
abstract ={Introduction: Health is one of the fundamental aspects of human life, and chronic diseases pose a serious threat in this realm, entailing far-reaching consequences. With rapid advancements in artificial intelligence, intelligent medical diagnostic systems have seen significant development, facilitating early diagnosis, cost reduction, and more accurate clinical decision-making. Despite the application of various data mining techniques, the complexity and high dimensionality of medical data remain challenging. In this context, deep learning methods and generative models serve as powerful tools for analyzing such data. Methods: This study proposes a novel method for improving the diagnosis of liver diseases, based on data preprocessing and augmentation using Generative Adversarial Networks (GANs) alongside a stacking ensemble learning approach. The Indian Liver Patient Dataset (ILPD) was used for evaluation. In the initial phase, the ensemble model achieved an accuracy of 77% using 10-fold cross-validation. Subsequently, the model was retrained using a GAN-based data augmentation technique, yielding an impressive accuracy of 99%. Results: The results indicate that the appropriate application of preprocessing and data augmentation methods has a significant impact on improving model performance and enhancing the accuracy of early liver disease detection. Conclusion: The proposed intelligent framework successfully overcomes the challenges of complexity and data imbalance in medical datasets, delivering a highly accurate and reliable model. This approach can serve as an efficient clinical decision-support system within the field of health informatics, offering significant assistance to healthcare professionals in the early diagnosis and improved management of liver diseases.},  
Keywords = {Early Liver Disease Diagnosis, Deep learning, Adversarial Generating Network, Machine Learning},
volume = {13},
Number = {1}, 
pages = {12-23}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.09},
url = {http://jhbmi.ir/article-1-941-en.html},  
eprint = {http://jhbmi.ir/article-1-941-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {HooshmandMoghadam, Balal and Keshtidar, Mohammad and Gholamian, Javad and Talebpour, Mahdi},  
title = {Digital Transformation in Iranian Sports Clubs to Attract Older Adults: A Narrative Review and Conceptual Model Proposal}, 
abstract ={Introduction: Today, digital transformation in senior sports clubs—an innovative approach to enhancing physical activity and the physical health of older adults—has attracted global attention. However, in Iran a comprehensive conceptual framework for the optimal utilization and localization of digital technologies in these clubs has not yet been designed. The aim of this study was to provide a narrative review of exergames and wearable technologies, with an emphasis on local challenges, and to present a conceptual model for digital transformation in Iranian sports clubs to attract older adults. Method: This study was designed as a structured narrative review employing an interdisciplinary approach. A systematic search of Persian and English sources published between 2010 and 2025 was conducted across reputable national and international databases. Keywords were selected with a focus on digital transformation, sports for older adults, wearable technologies, and sports clubs. Following screening and final assessment, 58 sources were selected for qualitative analysis and the synthesis of concepts. Results: Findings indicated that the majority of reviewed studies associated exergames and wearable technologies with improved motor motivation, increased adherence to physical activity, enhanced monitoring of health indicators, and personalization of older adults’ exercise programs. Analysis of selected studies also identified the main barriers to digital transformation in Iran’s senior sports clubs as weak technological infrastructure, high costs of digital equipment, limited digital literacy among older adults, and lack of coherent national policy. Accordingly, the study’s proposed conceptual model delineated four key dimensions — infrastructure, technology, human resources, and policymaking — as the principal pillars of digital transformation for Iranian senior sports clubs. Conclusion: The results of this review indicate that the success of digital transformation in Iranian senior sports clubs is not solely dependent on technology development; rather, it requires strengthening local infrastructure, enhancing older adults’ digital literacy, training specialized human resources, and formulating integrated national policies. The proposed conceptual model can serve as a practical framework for planning and developing technology-based senior sports clubs in Iran.},  
Keywords = {Digital transformation, Physical activity in older adults, E-sports, Wearable technologies, Sports clubs, Digital health, Iran},
volume = {13},
Number = {1}, 
pages = {24-40}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.10},
url = {http://jhbmi.ir/article-1-961-en.html},  
eprint = {http://jhbmi.ir/article-1-961-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Farboud, Mohsen and Hessam, Somayeh and Vahdat, Shaghayegh and Tofighi, Shahram},  
title = {Analysis of Barriers and Facilitators of Predictive Models for Metabolic Syndrome Prevention in Iran: A Qualitative Thematic SWOT}, 
abstract ={Introduction: As one of the most common metabolic disorders in Iran, metabolic syndrome imposes a heavy financial and therapeutic burden on the healthcare system and insurance organizations. Although AI-based predictive models can serve as valuable tools for early prevention, their success depends on their implementability within the real-world operational environment of the healthcare system. This study aims to examine the perspectives of healthcare experts regarding the facilitators and barriers associated with implementing these models for the prevention of metabolic syndrome in Iran. Method: This study employed a qualitative approach and a descriptive-analytical method, utilizing Braun and Clarke’s (2006) six-phase thematic analysis. The research design followed a hybrid inductive-deductive method. Purposive sampling was conducted until theoretical saturation was reached, ultimately involving 15 experts: six clinicians, five managers and policymakers, and four health information technology specialists. Data were collected through in-depth, semi-structured interviews and analyzed using manual coding. Additionally, the SWOT analysis framework served as a deductive guide for organizing and analyzing the themes. Results: The analysis led to the identification of 20 main themes, which were organized into four categories: strengths (8 themes), weaknesses (5 themes), opportunities (3 themes), and threats (4 themes). Strengths mainly included managerial flexibility, employee empowerment, and action-based learning. Weaknesses included dependence on traditional training and lack of continuous feedback, opportunities included technological advances and policy support, and threats included high workload and policy instability. Four implementation strategies (SO), WO, ST, WT) were extracted, with the combined SO and WT strategies being the most emphasized. Conclusion: The success of predictive models requires a focus on integrated strategies at the organization level and macro-policy making. The findings of this study can also contribute to evidence-based policy making in the Iranian Health Insurance Organization as well as national prevention programs.},  
Keywords = {Statistical Models, Qualitative Research, Strategic Planning, Health Services Accessibility, Barriers to Care},
volume = {13},
Number = {1}, 
pages = {41-52}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.11},
url = {http://jhbmi.ir/article-1-983-en.html},  
eprint = {http://jhbmi.ir/article-1-983-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Haghdoost, Ali Akbar and Dehnavieh, Rez},  
title = {Reconceptualizing Higher Education Governance in the AI Era: The Necessity of a Shift Toward Foresighted and Intelligent Governance}, 
abstract ={The article is a letter to the editor, so it does not have an abstract.},  
Keywords = {Artificial Intelligence, Higher Education, Medical Education, Anticipatory Governance, Future of Work, Educational Transformation},
volume = {13},
Number = {1}, 
pages = {53-58}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.12},
url = {http://jhbmi.ir/article-1-995-en.html},  
eprint = {http://jhbmi.ir/article-1-995-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Madadizadeh, Farzan and Ghaemi, Mohammad Mahdi and Agharezaei, Zhil},  
title = {The Role of Image Processing and Machine Learning in the Transformation of Medical Diagnostics}, 
abstract ={The article is a letter to the editor, so it does not have an abstract.},  
Keywords = {Image processing, Machine Learning, Deep convolutional neural network, Medical diagnosis},
volume = {13},
Number = {1}, 
pages = {59-63}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2026.13},
url = {http://jhbmi.ir/article-1-992-en.html},  
eprint = {http://jhbmi.ir/article-1-992-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}

@article{ 
author = {Radan, Ahmad and MirShahVelayati, Farzaneh and Khorrami, Fari},  
title = {Beyond Digitalization: The Imperative for Transitioning to a Digital Health Ecosystem in Iran}, 
abstract ={ },  
Keywords = {Digital Health Ecosystem, Artificial Intelligence (AI), Data Governance},
volume = {13},
Number = {1}, 
pages = {73-76}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {https://dx.doi.org/10.34172/jhbmi.2026.15},
url = {http://jhbmi.ir/article-1-1002-en.html},  
eprint = {http://jhbmi.ir/article-1-1002-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2026}  
}