@article{ 
author = {Hedyehzadeh, Mohammadreza and Yousefi, Mahdi},  
title = {Application of Machine Learning Methods to Predict the Survival Rate of Glioblastoma Patients Using MR Images}, 
abstract ={Introduction: In this study, a method for automatic prediction of the survival rate of patients with glioblastoma tumor based on machine learning methods and MRI images is presented. Method: The data set used in this study is the BraTS 2017 database with 163 samples. Each sample of database images has four different imaging modalities as well as information such as the patient's overall life expectancy according to the patient's day and age. Database images are labeled into three categories, short-term, medium-term, and long-term, based on patient longevity after treatment. To improve the prediction results, different types of features were extracted and taught by different machine learning methods. The considered features include texture, volumetric, statistical, and deep features. The machine learning methods used include support vector machine, nearest neighbors, linear discriminant analysis, and decision tree. Results: The best prediction accuracy based on the classification was obtained using deep features extracted by a pre-trained convolutional neural network (CNN) and by linear discriminant analysis. Conclusion: Deep learning approaches showed a good performance in the prediction of medical parameters such as survival rate time.},  
Keywords = {Survival Time, Deep Learning, Glioblastoma, Classification, MRI},
volume = {11},
Number = {1}, 
pages = {1-13}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.09},
url = {http://jhbmi.ir/article-1-828-en.html},  
eprint = {http://jhbmi.ir/article-1-828-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Mohammadi, Alireza and Nasiri, Parya and Moghabeli, Roy},  
title = {Spatio-temporal Analysis of Asthma Prevalence in the Tehran Metropolis Using Geographic Information Systems (GIS)}, 
abstract ={Introduction: Tehran, one of the world's most polluted cities, faces numerous health challenges, including respiratory diseases. Identifying high-risk areas for specific diseases at different periods is crucial for effective health management within the community. Method: This applied and descriptive-analytical study employed spatial statistics and GIS to conduct a spatio-temporal analysis of asthma in the Tehran metropolis. The statistical population comprised 1,473 individuals suffering from asthma across 355 neighborhoods within Tehran's legal boundaries between 2017 and 2020. To identify and understand the spatial patterns of asthma, techniques such as central mean and standard deviation ellipse, nearest neighbor index, hot spot analysis, global and local Moran's index, and kernel density estimation were utilized to analyze the spatial distribution patterns within the GIS environment. Results: The findings revealed a uniform nearest neighbor index in the spatial pattern analysis. The kernel density estimation model showed that in 2017, the highest asthma distribution was observed in the southwestern neighborhoods of Tehran. In 2018, the concentration shifted to the center, southeast, and southwest regions. In 2019, the southeastern areas were most affected, while in 2020, the southeastern and southern parts of Tehran exhibited the highest asthma prevalence. Conclusion: A comprehensive understanding of asthma hotspots, spatial patterns, and geographical distribution of health has been achieved at the neighborhood level in Tehran, which can be considered in preventive measures.},  
Keywords = {Spatio-temporal analysis, asthma, geographic information system (GIS), Tehran city},
volume = {11},
Number = {1}, 
pages = {14-25}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.10},
url = {http://jhbmi.ir/article-1-833-en.html},  
eprint = {http://jhbmi.ir/article-1-833-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Motamed, Sara and Askari, Elham},  
title = {Diagnosing Heart Disease Using Convolutional Neural Network and the Particle Swarm Optimization}, 
abstract ={Introduction: The human heart is a physiologically vital organ whose signals can be continuously recorded using an electrocardiogram (ECG) device. Cardiovascular diseases are one of the leading causes of mortality worldwide. Timely and accurate identification of this condition, along with preventive measures, can help reduce the incidence of cardiovascular diseases. Method: This article aimed to predict a group of heart failures based on the patterns found in extracted features from patients with cardiac arrhythmias, distinguishing them from samples in a normal state. The proposed model involves preprocessing operations, such as discretization and replacement of missing values using column-wise averaging, on the dataset. Subsequently, feature selection operations were performed on normalized data to reduce complexity and improve speed and accuracy. The data is then fed into decision tree classifiers, k-nearest neighbors, naive Bayes, and convolutional neural networks. Results: A comparison of the accuracy obtained from different algorithms before and after applying the proposed method reveals improved performance across all methods after implementation. Particularly, the convolutional neural network demonstrates superior performance. Conclusion: Based on the results, it can be concluded that the proposed model achieves an accuracy of 92.34%, surpassing other methods.},  
Keywords = {Cardiac Arrhythmia, Convolutional Neural Network, Particle Swarm Optimization, Multiclass Classification},
volume = {11},
Number = {1}, 
pages = {26-42}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.11},
url = {http://jhbmi.ir/article-1-840-en.html},  
eprint = {http://jhbmi.ir/article-1-840-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Oviesi, Safura and Tarokh, Mohammad Jafar and Momeni, Mohammad Kazem},  
title = {Automatic Diagnosis of Pulmonary Emphysema Using Optimized Unet-based Deep Neural Network}, 
abstract ={Introduction: Pulmonary emphysema is one of the lung diseases that usually remains unknown until old age and does not have a definitive treatment. A quick diagnosis of this disease helps a lot to the people involved in this disease and prevents the growth of emphysema masses. This research tries to in early diagnosis of this disease with the help of deep learning methods. Method: This research tries to diagnose this disease faster with the help of Unet neural network optimized with GPC meta-heuristic algorithm. The data of this research were collected from Imam Ali and Bu Ali Sina hospitals, Zahedan city, Sistan and Baluchistan province. The data include 300 pieces with emphysema, including 65 cases of CLE, 97 cases of PSE, 138 cases of PLE, and 45 cases of normal data. These data were analyzed by Unet deep neural network and GPC optimization algorithm, and finally, with the help of accuracy criteria, recall, specificity, and F-measure were compared and investigated with other methods. Results: In this research, the criteria used have much better results compared to other emphysema diagnosis methods with the help of the optimized Unet network, with accuracy of 18.97, prediction of 40.98, sensitivity of 48.23, and f score of 97.50, respectively, which shows a faster, more accurate, and more effective diagnosis of this disease with the help of the proposed method. Conclusion: Using the right deep learning methods in combination with strong optimization algorithms can enable faster and more accurate treatment of diseases.},  
Keywords = {Artificial intelligence, Neural networks, Pulmonary emphysema, Image processing, Deep Learning},
volume = {11},
Number = {1}, 
pages = {43-59}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.12},
url = {http://jhbmi.ir/article-1-841-en.html},  
eprint = {http://jhbmi.ir/article-1-841-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Afrasiabi, Mahlagha and Movahedi, Ahm},  
title = {Alzheimer\'s Prediction Using Deep Neural Network and Particle Swarm Optimization}, 
abstract ={Introduction: Alzheimer's disease is an irreversible neurological condition characterized by cognitive, behavioral, and memory impairments. Early prediction before the transition from mild cognitive impairment to Alzheimer's disease is still a challenging issue. This study aimed to identify factors associated with Alzheimer's disease. Method: This study proposes a framework for predicting Alzheimer's disease using data collected from the OASIS project, made available by the Washington University Research Center. In this study, a deep neural network was used for prediction. A particle swarm optimization (PSO) algorithm was employed for selecting appropriate features. The combination of these two methods increases the accuracy of the proposed prediction method. Results: The results indicate that the proposed method achieves higher accuracy with fewer features. Among the 11 features in this dataset, six features (age, socioeconomic status, Mini-mental state examination score, clinical dementia rating scale, estimated total intracranial volume, and normalized whole-brain volume) have a significant impact on predicting the disease. Among these six features, the clinical dementia rating scale is of great importance. Conclusion: This study investigated the influential factors and prediction of Alzheimer's disease. Early diagnosis of Alzheimer's disease allows for the provision of appropriate diagnostic and therapeutic services, as well as an improvement in patients' quality of life. The proposed method in this study is compared with various machine learning algorithms that have shown good accuracy in predicting Alzheimer's disease. The results indicate that the accuracy of the proposed method is higher with fewer features.},  
Keywords = {Alzheimer's disease, deep neural network, machine learning, particle swarm optimization},
volume = {11},
Number = {1}, 
pages = {60-71}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.13},
url = {http://jhbmi.ir/article-1-849-en.html},  
eprint = {http://jhbmi.ir/article-1-849-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Einipour, Ami},  
title = {Providing a Multi-Scale Self-Encoding Method to Improve Clustering and Analysis of Single-Cell Data}, 
abstract ={Introduction: In bioinformatics, analyzing single-cell data is crucial for understanding cellular functions' complexities. However, this analysis faces challenges like inefficient dimensionality reduction and suboptimal clustering. This study aimed to present a method that enhances the clustering of single-cell data, improves reconstruction quality, and reduces data dimensions. Method: This paper introduces SAMS (Single-cell Analysis using Multi-Scale Autoencoder), which uses a multi-scale autoencoder model to improve the challenges in single-cell data analysis. The SAMS method involves three primary steps: (1) data preprocessing and normalization, (2) employing a deep neural network model to reconstruct and reduce data dimensions with the help of a multiscale autoencoder, and (3) clustering the reduced data using the K-means algorithm to assess the method's performance. Results: The SAMS method was implemented using Python on single-cell datasets. The results demonstrate that SAMS can effectively visualize cells in a two-dimensional space with an average Nearest Neighbor Error (NNE) rate of 89%, indicating a strong preservation of data structure. Additionally, the Silhouette index and Davis-Bouldin index, which measure clustering accuracy, show significant improvement with averages of 0.66 and 0.50, respectively. Conclusion: The proposed SAMS method by combining the multiscale self-encoder model and the K-means algorithm could obtain better results than the previous methods. Its application in single-cell data analysis can aid researchers in gaining deeper insights into cellular functions and discovering new patterns.},  
Keywords = {Single-cell analysis, Dimensionality reduction, Clustering analysis},
volume = {11},
Number = {1}, 
pages = {72-82}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.14},
url = {http://jhbmi.ir/article-1-867-en.html},  
eprint = {http://jhbmi.ir/article-1-867-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Kanani, Shiva and Mahdavi, Iraj and Ziaie, Naghmeh and RahimpourCami, Bagher},  
title = {Prediction of 30-day Mortality for Heart Failure Patients with Cardiogenic Shock Using Random Forest Hyperparameter Tuning}, 
abstract ={Introduction: Heart failure is a clinical syndrome resulting from structural or functional abnormalities of the heart, leading to reduced cardiac output or increased intracardiac pressure. When combined with cardiogenic shock, it becomes an emergency condition with a high mortality rate, necessitating immediate diagnosis and treatment. Accurate prediction of 30-day mortality in these patients is vital for timely care and patient survival. This study aimed to optimize the Random Forest algorithm by adjusting hyperparameters to more accurately predict 30-day mortality in heart failure patients with cardiogenic shock. Method: In this research, data from 201 cardiac patients aged over 18 years who experienced cardiogenic shock at Rouhani Hospital in Babol in 2020, were used. Thirty-four selected features such as age, history of cardiac surgery, pH, lactate levels, diabetes, etc., were examined, and their one-month mortality was tracked through telephone follow-ups. Results: The results showed that increasing age (above 57 years), decreasing pH (below 7.3), and elevating lactate levels (above 2) significantly increased the risk of 30-day mortality. By optimizing the hyperparameters of the Random Forest algorithm (ntree=1000 and mtry=14), prediction accuracy improved from 66.0% to 71.8%. Conclusion: This study demonstrates that the accuracy of the Random Forest algorithm depends on its input hyperparameters and that optimizing these parameters can lead to a more precise prediction of mortality in heart failure patients with cardiogenic shock. With appropriate optimization, this algorithm can serve as an effective tool for the early detection of high-risk patients and timely provision.},  
Keywords = {Heart Failure, Cardiogenic Shock, Random Forest, Hyperparameter Tuning, Mortality prediction},
volume = {11},
Number = {1}, 
pages = {83-95}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.15},
url = {http://jhbmi.ir/article-1-868-en.html},  
eprint = {http://jhbmi.ir/article-1-868-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Abdekhoda, Mohammadhiw},  
title = {Article Retraction: A Tool to Deal with Information Pollution}, 
abstract ={The article is a letter to the editor, so it does not have an abstract.},  
Keywords = {Article Retraction, Information Pollution},
volume = {11},
Number = {1}, 
pages = {96-100}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.16},
url = {http://jhbmi.ir/article-1-884-en.html},  
eprint = {http://jhbmi.ir/article-1-884-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Samsami, Mohamahd Amin and Rashidi-Khazaee, Parviz and BolokiSpeily, Omid Rez},  
title = {Providing and Implementing the Secure Structure of the Waiting List Management System for Organ Transplantation Patients with Blockchain Technology based on IPFS}, 
abstract ={Introduction: In recent decades, the medical field has faced many challenges in managing and maintaining information security on waiting lists for organ transplantation patients. The lack of trust of the participants in this process, which requires more efforts to ensure the security and privacy of patients and donors, highlights the necessity for finding new security solutions. Method: In this study, a safe and reliable method was presented and implemented using blockchain technology based on the Network Distributed File System (IPFS) to improve waiting list management and solve the challenge of maintaining voluminous medical documents. In this method, patient information is anonymized using appropriate algorithms, and then, stored and linked to the blockchain network. Results: The implemented system provides an integrated and innovative method in organ transplantation management, which not only comprehensively provides the security and privacy of patients, but also reduces social inequalities and increases trust in the organ donation process, as well as fair management of patient waiting lists. Conclusion: The proposed system has provided a scalable and efficient system using microservices architecture and can be provided as a reliable tool for medical stakeholders.},  
Keywords = {Blockchain, Organ Transplant Waiting List, Anonymization of Medical Documents, Storage of Patients' Medical Documents, Patient Privacy security},
volume = {11},
Number = {2}, 
pages = {101-114}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.17},
url = {http://jhbmi.ir/article-1-843-en.html},  
eprint = {http://jhbmi.ir/article-1-843-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Arad, Fereshteh and Mousavi, Seyyed Mohammad and Hosseini, Soodeh and Amizade, Maryam and Sheikhi, Ayyub},  
title = {Intelligent Diagnosis of Larynx Cancer Using Machine Learning Methods}, 
abstract ={Introduction: Larynx cancer can be benign or malignant based on various factors. This research aimed to provide a machine learning-based model to improve the diagnosis of individuals with larynx cancer. Method: In the first step, the voices of the people who visited the medical centers (including the sounds (A), (E), and (O)) were recorded and considered as a data set. In the second step, the data were classified into three classes (benign cancer, malignant cancer, and healthy) by a specialist. In the third step, the data cleaning was done. In the fourth step, the features related to sound were extracted from the data. In the fifth step, five machine learning models including SVM, Decision Tree, Naïve Bayes, MLP, and Random Forest were implemented on the data set. Finally, the performance of the models was evaluated using evaluation criteria such as accuracy, F-score, and other evaluation criteria. Results: The results of the implementation showed that the SVM model had a higher accuracy than other models for the sound (A) and sound (O) with an accuracy of 0.818, and the sound (E) with an accuracy of 0.818 in the model MLP had the highest accuracy. Conclusion: The present study evaluated machine learning models for the diagnosis of laryngeal cancer based on audio data. The results showed that the use of the SVM model for the diagnosis of laryngeal cancer can help diagnose this disease more accurately and provide reliable results.  },  
Keywords = {Laryngeal cancer, Machine learning, Deep learning, Artificial intelligence},
volume = {11},
Number = {2}, 
pages = {115-130}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.18},
url = {http://jhbmi.ir/article-1-853-en.html},  
eprint = {http://jhbmi.ir/article-1-853-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Nizamali, Mehdi and Mohammadi, Mahnaz and NezhadAliLampajani, Masoumeh},  
title = {Bioinformatics Analysis of Metabolic Signaling Pathways in Glioblastoma Cancer Stem Cells}, 
abstract ={Introduction: Glioblastoma is one of the common brain cancers that has a high mortality rate. In this study, the genes present in the metabolic pathways of glioblastoma stem cells were examined and nominated using bioinformatics analysis. Method: In this study, an appropriate dataset was selected for analysis by referring to GEO database. This dataset included gene expression profiles in stem cells isolated from glioblastoma patients. Gene clusters with high and low expression were categorized. Rich databases such as Enrichr, STRING, and GEPIA were used for more accurate data evaluation. Finally, candidate genes were isolated. Results: 1250 genes were highly expressed in cholesterol biosynthesis, inositol triphosphate metabolism, geranyl diphosphate metabolism, zymosterol biosynthesis, and phosphatidylinositol metabolism, and 1030 genes were low in chondroitin sulfate, dermatan sulfate, N acetyl glucosamine, and glycolysis pathways. After evaluating the relationship between protein networks, genes with high and low expression were selected. All these genes were observed in the survival curve, in about 20 months. The survival rate of patients was less than 10%. Conclusion: The results of this study showed that the DHCR7 gene had a significant increase in expression. However, the ENO2 gene had a significant decrease in expression. The rest of the genes had a relative increase and decrease in expression.},  
Keywords = {Glioblastoma, Cancer Stem Cells, Gene Expression Profiles, Bioinformatics Analysis},
volume = {11},
Number = {2}, 
pages = {131-148}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.19},
url = {http://jhbmi.ir/article-1-871-en.html},  
eprint = {http://jhbmi.ir/article-1-871-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Dinari, Fatemeh and Bashiri, Azadeh and Raufi, Raziyeh and Moulai, Khadijah and Moulai, Rez},  
title = {Identifying and Introducing the Design Requirements of a Mobile Self-Management Application for Hemodialysis Patients: A Cross-Sectional Study}, 
abstract ={Introduction: Hemodialysis is one of the most common methods of treating patients with chronic kidney failure. People undergoing hemodialysis face various self-management challenges such as physical, emotional, and psychological problems. Mobile phone applications are useful tools for the self-management process of chronic patients. The purpose of this study was to identify and introduce the requirements needed in designing a mobile phone-based self-management application for hemodialysis patients. Method: This descriptive cross-sectional study was conducted in 2024. The data collection tool was a researcher-made electronic questionnaire that included information-educational needs and application capabilities. A needs assessment was conducted on 33 people (11 doctors and 22 patients) in Afzalipour Hospital in Kerman. Data analysis was done using SPSS version 27 and descriptive statistics. Results: A total of 89 information-educational needs and application capabilities were identified for hemodialysis patients. The data elements identified for application capabilities were divided into 10 groups: patient profile, clinical data, medication administration, nutrition and dietary management, psychological and psychiatric challenges management, sleep management, pain management, communication, and exercise. According to the views of doctors and patients, all information-educational needs (26 data elements) and application capabilities (63 data elements) except surname, national code, and contact number, with an average greater than and equal to 2.50 (50 percent) were considered essential for designing an application program. Conclusion: The identified information-educational needs and the application capabilities in this study, can assist designers in creating efficient and user-friendly self-management applications for hemodialysis.},  
Keywords = {Hemodialysis, Self-management, Application program, Mobile health},
volume = {11},
Number = {2}, 
pages = {149-161}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.20},
url = {http://jhbmi.ir/article-1-888-en.html},  
eprint = {http://jhbmi.ir/article-1-888-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Nabizadeh, Amir Hossei},  
title = {Digital Twins in Healthcare}, 
abstract ={The article is a letter to the editor, so it does not have an abstract.},  
Keywords = {Digital Twins},
volume = {11},
Number = {2}, 
pages = {162-165}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.21},
url = {http://jhbmi.ir/article-1-879-en.html},  
eprint = {http://jhbmi.ir/article-1-879-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Ahmadipour, Alihasan and Sarafinejad, Afshi},  
title = {Investigation of Drug Interactions through Analysis of Prescribed Medications Association Rules Using the FP-growth Algorithm}, 
abstract ={Introduction: The discovery of hidden patterns in pharmaceutical data can contribute to improving the performance of hospital pharmacies. One of the applications of advanced data analysis techniques is the identification of drug interactions. Method: This study was conducted using data mining techniques with the FP-growth algorithm in the RapidMiner Studio® 10.1 environment to extract association rules and frequent pharmaceutical patterns. Data preprocessing and modeling were performed based on the CRISP-DM model. The type and level of drug interactions were determined based on the algorithm's results and by referencing the database at www.drugs.com. Results: The results included 17 association rules and 126 prescribing patterns, ranging from single-drug to four-drug combinations. Of the 64 two-drug prescribing patterns, 56 had no interaction, 6 had moderate interactions, 1 had a minor interaction, and 1 had a major interaction. Additionally, of the 19 three-drug patterns, 18 had no interaction, and only 1 had a moderate interaction. No interactions were observed in the four-drug prescribing pattern. Conclusion: The findings of this study can assist stakeholders in improving the pharmaceutical supply chain, optimizing prescriptions, reducing drug interactions, and lowering costs. The discovered patterns may also be used as part of a clinical decision support system. Although no significant drug interactions were observed in this study, the discovery of even one major interaction highlights its importance and further underscores the practical role of computer applications in medicine.  },  
Keywords = {FP-Growth, CRISP-DM, Drug Interactions, RapidMiner, Association Rules},
volume = {11},
Number = {2}, 
pages = {166-175}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.22},
url = {http://jhbmi.ir/article-1-886-en.html},  
eprint = {http://jhbmi.ir/article-1-886-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Nikokaran, Jalal and FatemiAghda, Seyed Ali and Dehghan, Hamidreza and Mirfakhraei, Mojgan and Poornematy, Mohammad Mehdi and Mirfakhraei, Mahdi},  
title = {Usability Evaluation of the Ministry of Health Exam System: Viewpoint the Users}, 
abstract ={Introduction: Electronic education has increased due to the growth of technology, the spread of the Coronavirus, and the need to maintain social distancing. Electronic test systems are used to evaluate students. This study aims to evaluate the usability of the test system from the users' perspective. Method: This research is a descriptive cross-sectional study. The statistical population was all the students and faculty members of Shahid Sadoughi University of Medical Sciences. A random sample of 318 students was selected, and faculty members were selected using a Census approach. The research data were obtained based on the standard questionnaire for evaluating the usability of Quiz version 5.5. Results: Students rated the usability of the test system as "average," whereas faculty members rated it as "good." Both groups identified "Learning by the user" as the section with the highest average score. However, their opinions diverged regarding the lowest-scoring section: students indicated "system capabilities," while faculty members pointed to "terminology and system information." Conclusion: The findings of this research suggest that more attention should be given to students' opinions regarding the electronic testing system, and its problems should be addressed. Furthermore, conducting additional studies with other universities could enhance the system and increase user satisfaction.},  
Keywords = {Online test, Usability Evaluation, E-Education, e-test, E-Test, Technical issues of the exam, University of Medical Sciences},
volume = {11},
Number = {3}, 
pages = {176-185}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.23},
url = {http://jhbmi.ir/article-1-874-en.html},  
eprint = {http://jhbmi.ir/article-1-874-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {MousaviKardar, Seyedeh Fatemeh and Mohammadi, Mahnaz and Esfandiari, Behnaz},  
title = {Investigating the microRNAs Involved in the Acute Phase of Hepatocellular Carcinoma Caused by Hepatitis C Virus}, 
abstract ={Introduction: Hepatocellular carcinoma is one of the most common cancers in the world. In this study, we examined and nominated the genes present in the pathways of hepatocellular carcinoma associated with HCV using bioinformatics analysis. Method: The appropriate dataset for analysis was selected from the GEO database. This dataset included gene expression profiles in hepatocellular carcinoma associated with HCV. Gene clusters with high and low expression levels were categorized. Rich databases such as Enrichr, STRING, and GEPIA were also used. Finally, the candidate genes were isolated. Results: A total of 512 genes with high expression and 500 genes with low expression were involved in the progression pathways of hepatocellular carcinoma. The pathways associated with the cell cycle, cell adhesion, AMPK, PPAR, and MAPK were clearly observed. After evaluating the relationship between protein networks, ADH4, FBP1, and ACS1 showed increased expression, while CDK4, E2F1, and MAPK3 genes displayed decreased expression. All these genes were noted in the survival curve; over a period of about 15 months, the survival rate of patients was less than 20%. miR-21-5p, hsa-miR-24-3p, and hsa-miR-25-3p were significantly more effective in regulating these genes. Conclusion: Bioinformatics analyses of key and important genes were introduced through the examination of gene expression profile data. ADH4, FBP1, and ACS1 genes showed increased expression, whereas the CDK4, E2F1, and MAPK3 genes displayed decreased expression, which may play an important role in targeting the genes involved in hepatocellular carcinoma associated with HCV.},  
Keywords = {Hepatocellular Carcinoma, MiRNAs, Gene Expression Profile, Bioinformatics Analysis, Hepatitis C virus},
volume = {11},
Number = {3}, 
pages = {186-202}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.24},
url = {http://jhbmi.ir/article-1-875-en.html},  
eprint = {http://jhbmi.ir/article-1-875-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Nabizadeh, Amir Hossein and Ghaemi, Mohammad Mehdi},  
title = {Digital Twins in Healthcare: Necessities, Pros, Cons, and Future Directions}, 
abstract ={Introduction: Digital Twin (DT) refers to a digital model of a physical product, system, or process used for simulation, monitoring, and optimization. In healthcare, this technology has transformed patient care by generating virtual models of patients, enabling the prediction and optimization of health outcomes. This study examines the benefits, challenges, implementation requirements, and future directions of this technology in healthcare, aiming to review and summarize prior studies while avoiding duplication of research. Method: This study employs a narrative approach in the context of digital twins in healthcare. It does not follow a systematic methodology but rather relies on existing models, hypotheses, and personal expertise to derive general conclusions. Results: This study describes how DT has facilitated personalized medicine, improved surgical outcomes, managed chronic diseases, streamlined clinical trials, and optimized hospital operations. Additionally, it highlights challenges associated with DT in healthcare, such as ethical implications, privacy concerns, and regulatory issues. It emphasizes the need for strong data governance and interdisciplinary collaboration to fully leverage the potential of DT. Finally, this research explores key areas for the future development of this technology. Conclusion: The requirements, challenges, benefits, and future directions presented in this study can guide the design and implementation of accurate and intelligent DT in the healthcare domain},  
Keywords = {Digital Twins, Healthcare, Artificial Intelligence},
volume = {11},
Number = {3}, 
pages = {203-213}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.25},
url = {http://jhbmi.ir/article-1-883-en.html},  
eprint = {http://jhbmi.ir/article-1-883-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {FallahPour, Roya and Jamaldini, Seyed Hamid and AllahyariFard, Najaf},  
title = {Investigating the Interaction of Herbal Medicinal Compounds Against Mpro Protease in SARS-CoV-2 Virus Strains}, 
abstract ={Introduction: COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. The identification of beneficial plant compounds and their use for targeting important proteins involved in the infection and replication of the coronavirus is considered an effective option in the fight against this disease. The aim of this research was to investigate the interaction of native medicinal plant compounds against the Mpro protease of different SARS-CoV-2 strains and to determine and evaluate the final compound using the HPLC method. Methods: The three-dimensional structure of the Mpro protease was obtained from the PDB database. Various antiviral plant compounds were collected from databases and articles. Molecular docking was performed using AutoDock Vina. The features and properties of the selected compounds were examined using different servers. Mutagenesis for the methionine amino acid 49 was performed, and a new three-dimensional structure of the Mpro protein was modeled for different virus strains. Docking analysis for four selected compounds was conducted based on binding energy. Molecular dynamics simulation (MD) was carried out to examine the stability of the final structure, and HPLC was used to evaluate the presence of the effective compound in the desired plant. Results: Docking results showed that the compound Cyanidin-3, 5-di-o-glucoside is effective in inhibiting Mpro, as indicated by its favorable total binding energy. The pharmacokinetic properties of this compound were also determined. MD results indicated that the Mpro-Cyanidin-3, 5-di-o-glucoside complex is stable. HPLC confirmed the presence of Cyanidin-3, 5-di-o-glucoside in Iranian pomegranate extract. Conclusion: The compound Cyanidin-3, 5-di-o-glucoside found in Iranian pomegranate extract can bind to Mpro with high affinity and inhibit its activity, potentially serving as a drug that directly targets the coronavirus.},  
Keywords = {Mpro Protein, Coronavirus, Native plants, Molecular docking, Molecular dynamics},
volume = {11},
Number = {3}, 
pages = {214-228}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.26},
url = {http://jhbmi.ir/article-1-876-en.html},  
eprint = {http://jhbmi.ir/article-1-876-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Kanani, Shiva and Mahdavi, Iraj and Ziaie, Naghmeh and RahimpourCami, Bagher},  
title = {Identification of Important Features to Predict 30-Day Mortality in Heart Failure Patients with Cardiogenic Shock}, 
abstract ={Introduction: Cardiovascular diseases remain a leading global cause of mortality, with ischemic heart disease projected to account for 23.3 million deaths by 2030. Heart failure and cardiogenic shock account for a significant proportion of these deaths and require timely treatment as medical emergencies. This study aims to predict mortality within one month in patients experiencing cardiogenic shock secondary to heart failure using a concise set of predictive features. Method: An analytical cross-sectional study was conducted at Babol Razi Hospital, involving 201 adult patients (≥18 years) treated for cardiogenic shock in 2020. Data from 34 clinical variables, including age, history of cardiac surgery, pH levels, lactate concentration, diabetes status, and blood pressure, were meticulously analyzed. Mortality outcomes within one month were assessed via structured telephone follow-up. Logistic regression and Gradient Boosting Machine (GBM) algorithms were used for predictive modeling. Results: The average age of patients was 69.44 ±15.71 years. Among them, 47.7% died. The study identified age, lactate levels, diabetes, and initial confusion as significant predictors of mortality risk. Each additional year of age was associated with a 7% higher probability of mortality. Diabetic patients faced more than double the mortality risk compared to non-diabetics. Confusion at presentation increased the mortality risk fourfold, while elevated lactate levels raised it by 1.5 times. Conclusion: Logistic regression and GBM algorithms demonstrated effectiveness in predicting one-month mortality among cardiogenic shock patients with heart failure based on selected features. This approach holds promise for improving referral processes and reducing costs in healthcare settings.},  
Keywords = {Heart Failure, Cardiogenic Shock, Death Prediction, Logistic Regression, Feature Selection},
volume = {11},
Number = {3}, 
pages = {229-243}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.27},
url = {http://jhbmi.ir/article-1-877-en.html},  
eprint = {http://jhbmi.ir/article-1-877-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Shakibaeinia, Abdolhossein and Chegin, Mohsen and Golabpour, Amin and Khosravi, Ahm},  
title = {Providing a Foresight Model for Selecting the Appropriate Breast Cancer Diagnosis Model}, 
abstract ={Introduction: Selecting an appropriate model for breast cancer diagnosis is critical. Unsuitable models can compromise diagnostic accuracy, lead to incorrect outcomes, and impact clinical decision-making. In this context, foresight models are valuable tools for identifying and selecting the most effective diagnostic models. The objective of this study was to identify optimal models for breast cancer detection using foresight models.  Method: This study began by extracting articles related to artificial intelligence-based breast cancer diagnosis. The number of articles associated with each algorithm was determined, and algorithms referenced in fewer than 50 articles were excluded. Subsequently, annual publication trends were analyzed. A time series model based on artificial neural networks was developed to predict research trends over the next two years and to identify the algorithms expected to receive more research attention.  Results: After applying the exclusion criteria, a total of 2,308 articles were categorized into eight groups: deep learning, artificial neural networks, support vector machines, fuzzy logic, clustering, decision trees, Bayesian methods, and logistic regression.  Additionally, eight time series models were constructed using data from the past seven years, predicting that deep learning and artificial neural networks will lead future research efforts in breast cancer diagnosis.  Conclusion: This study highlights the effectiveness of foresight as a methodological approach for selecting optimal techniques for breast cancer diagnosis. The results indicate that artificial neural networks and deep learning demonstrate superior performance and are likely to be pivotal methodologies for future research in this area.},  
Keywords = {Foresight, Diagnosis, Breast Cancer, Artificial Neural Network, Deep Learning},
volume = {11},
Number = {3}, 
pages = {244-256}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.28},
url = {http://jhbmi.ir/article-1-887-en.html},  
eprint = {http://jhbmi.ir/article-1-887-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Mohaghegh-Montazeri, Maryam and Gozali, Elahe and Jebraeily, Mohamad and Ahangar, Nematollah},  
title = {Identifying Effective Factors in the Electronic Prescribing System of Pharmacies from the Point of view of Selected Pharmacists and Pharmaceutical Technicians in Urmia}, 
abstract ={Introduction: The emergence of electronic prescriptions has transformed the methods of prescribing and distributing medications in pharmacies. The aim of this study is to explain the factors affecting workflow, patient communication, and medication safety within the electronic prescription system from the perspectives of pharmacists and pharmacy technicians. Method: This research is a descriptive and cross-sectional study conducted in 2023 in selected pharmacies in the city of Urmia. It was carried out in two stages. In the first stage, relevant studies were systematically reviewed and selected based on entry criteria to identify the influencing factors. In the second stage, the two-stage Delphi method was used with purposive and snowball sampling to finalize the identified factors. Results: The results of this study consisted of two stages. In the first stage, the factors affecting workflow, patient communication, and medication safety in pharmacies within the electronic prescription system were identified. In the second stage, the validity of the factors identified in the first stage was determined using a two-stage Delphi method. After two rounds of Delphi, 11 items related to workflow variables, 6 items for patient communication, and 11 items for medication safety were confirmed. Conclusion: This study has identified the factors that can be utilized by health policymakers and developers of electronic prescription systems to improve efficiency and serve as a framework for formulating both long-term and short-term plans.},  
Keywords = {Electronic Prescription, Workflow, Patient Communication, Medication Safety},
volume = {11},
Number = {3}, 
pages = {257-270}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.29},
url = {http://jhbmi.ir/article-1-890-en.html},  
eprint = {http://jhbmi.ir/article-1-890-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Hajesmaeel-Gohari, Sadrieh and Shafiei, Elaheh and Gholipoor, Maryam},  
title = {Mobile Health Interventions to Facilitate Shared Decision Making: A Systematic Review}, 
abstract ={Introduction: Shared Decision Making (SDM) is a process in which healthcare providers share evidence with patients, enabling them to make informed decisions based on their values and preferences. Mobile health (mHealth) can support SDM, leading to improved clinical outcomes. Therefore, this study aims to review the literature on mHealth interventions that facilitate SDM. Method: In November 2022, the PubMed, Scopus, and Web of Science databases were searched for papers on mHealth interventions that facilitate SDM. The first author's name, year of publication, country of the study, study objective, type of disease, type of mHealth intervention, and outcomes of the interventions were extracted from the selected studies. Descriptive statistics were used to analyze the data. Results: Of the 32 articles included in the study, most were published in 2021 (25%) and 2022 (21%). The majority of studies (43.5%) were conducted in the United States. Fifteen studies (47%) evaluated participants' satisfaction with using mobile health for shared decision-making, and fourteen studies (43.5%) measured the usability of mobile health. In most studies (84.5%), mobile-based applications were used for shared decision-making. Most studies assessed the impact of shared decision-making on musculoskeletal diseases (18.5%), cancer (15.5%), cardiovascular diseases (15.5%), and mental health disorders (12.5%). Most studies (97%) found that using mobile health had a positive impact on shared decision-making. Conclusion: mHealth assists patients in making informed decisions about their treatment by facilitating SDM. Patients can use this technology at home to overcome barriers posed by a lack of face-to-face visits. The findings of this research can guide healthcare providers in leveraging affordable and accessible technology to engage patients in selecting the best treatment options.},  
Keywords = {Shared Decision Making, Mobile Health, Systematic Review},
volume = {11},
Number = {3}, 
pages = {271-281}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2024.30},
url = {http://jhbmi.ir/article-1-893-en.html},  
eprint = {http://jhbmi.ir/article-1-893-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2024}  
}

@article{ 
author = {Kohzadi, Zeinab and Dabbagh, Ali and Taheri, Mehrdad and Emami, Hassan and Ghasemi, Mahshid and Kohzadi, Zahra and Rahmatizadeh, Shahabedi},  
title = {The Functional and Non-Functional Requirements of a Telemedicine System for Chronic Pain Management}, 
abstract ={Introduction: With the expansion of information and communication technologies, telemedicine systems have become a key tool in enhancing the quality of healthcare services. These systems enable the delivery of medical care without the need for physical presence of patients and healthcare providers, playing a significant role in the management of chronic diseases, particularly chronic pain. Effective chronic pain management requires continuous follow-up, easy access to services, and accurate information exchange between patients and the medical team. The successful design of a telemedicine system depends on the precise identification and evaluation of its functional and non-functional requirements. These requirements form the foundation for ensuring the quality, security, efficiency, and user acceptance of the system. The aim of this study is to identify these requirements to support the development of an effective system for chronic pain management. Method: This applied, cross-sectional study was conducted in two phases. In the first phase, existing processes related to chronic pain management in relevant hospitals were analyzed. Subsequently, focused group discussion (FGD) sessions were held with 15 experts, including four medical informatics specialists, seven anesthesiologists, and four pain fellowship specialists. Participants were purposefully selected based on availability. Sessions continued until data saturation was achieved, ensuring that all dimensions of the subject had been thoroughly discussed and confirmed by the expert panel. Results: Functional requirements were categorized into seven groups: user management (3 items), scheduling and communication (5 items), medical information management (3 items), pharmacy and e-prescription (2 items), communication and support (2 items), reporting and data analysis (2 items), and content and education (4 items). Non-functional requirements identified included reliability and availability, interoperability, security and privacy, safety, performance, and scalability. Conclusion: The findings of this study highlight the critical role of accurately identifying and understanding both functional and non-functional requirements in the success of telemedicine systems. Optimal system performance relies on the simultaneous consideration of features such as security, reliability, interoperability, and accessibility, alongside core clinical functionalities. Therefore, the design of such systems must adopt a comprehensive and multidimensional approach to effectively meet the needs of both patients and healthcare teams.},  
Keywords = {Telemedicine, Chronic Pain Management, Functional and Non-Functional Requirements},
volume = {11},
Number = {4}, 
pages = {282-290}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.01},
url = {http://jhbmi.ir/article-1-900-en.html},  
eprint = {http://jhbmi.ir/article-1-900-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Dehghandar, Mohammad and Nikbakht, Maryam and YousefiNadarabadi, Fateme},  
title = {Designing a Fuzzy Stable System to Estimate Peripheral Resistance Using Frequency and Pulse Strength Parameters of Persian Medicine}, 
abstract ={Introduction: Pulsology is one of the most important clinical diagnostic methods in Persian medicine (PM) and has been used by traditional medicine specialists for centuries to assess clients' health conditions. Additionally, photoplethysmogram (PPG) is a simple and non-invasive method for measuring changes in blood volume with each pulse, and the amount of peripheral resistance (PR), which is crucial for understanding patients' clinical conditions, can be extracted from it. Moreover, a system based on fuzzy rules serves as one of the methods for prediction and estimation. This research establishes the relationship between PR in PPG and PM pulsology through a fuzzy system.. Method: Fuzzy theory underlies many studies aimed at diagnosing diseases. To design a stable fuzzy system, pulse information and PPG signals from 35 individuals, recorded by a PM expert, were used. Complete, normal, consistent, and symmetric fuzzy sets sensitive to the range of input variables were defined to encompass the input and output spaces and to generate rules using the available data. Stability conditions for the system were also established. Results: A fuzzy system comprising 35 rules, triangular and trapezoidal membership functions, a singleton fuzzifier, a product inference engine, and a centroid defuzzifier was designed using MATLAB R2021b. This stable fuzzy system, with pulse frequency and pulse strength inputs, as well as PR output, operates within the defined range. The stability of the system is assured by the membership function definitions, with a maximum error of 0.01.  Conclusion: This intelligent stable fuzzy system provides a reliable estimate of the PR from the PPG signal by using PM pulse variables. It was observed that an increase in pulse frequency correlates with an increase in PR, while an increase in pulse strength correlates with a decrease in PR in the PPG. Therefore, when the photoplethysmography signal is unavailable, trained physicians can derive a suitable estimate of the patient's peripheral vascular resistance status based on the frequency and strength of the patient's pulse, which is valuable for the clinical evaluation and diagnosis of the patient's medical issues.},  
Keywords = {Stable fuzzy system, pulse, Persian medicine, photoplethysmogram, peripheral resistance},
volume = {11},
Number = {4}, 
pages = {291-305}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.02},
url = {http://jhbmi.ir/article-1-866-en.html},  
eprint = {http://jhbmi.ir/article-1-866-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Maghsoudi, Rouhollah and Tavan, Mehdi},  
title = {Differential Diagnosis of Precancerous and Cancerous Oral Lesions Based on an Integrated Artificial Intelligence Model}, 
abstract ={Introduction: Early detection of precancerous and cancerous oral lesions is essential for improving treatment outcomes and reducing patient mortality. However, the similarity of these lesions to benign lesions complicates the diagnostic process. Computer-aided diagnostic (CAD) systems can enhance diagnostic accuracy and reduce the time required for diagnosis. This study aimed to develop an integrated model for the diagnosis of oral lesions Method: In this study, clinical images of patients with leukoplakia, lichen planus, and oral squamous cell carcinoma were collected. After preprocessing the images to improve quality and remove noise, key features were extracted using the SURF algorithm. To reduce data dimensions and select effective features, the K-means clustering algorithm was employed, resulting in a reduction to 30 features. Image classification was performed using five machine learning algorithms: support vector machine (SVM), multilayer neural network (MLP), radial basis function (RBF), decision tree, and Bayesian classifier. The performance of these algorithms was evaluated comparatively. Results: The results indicated that SVM excelled in detecting oral lesions, achieving an accuracy of 95%. This algorithm demonstrated significant superiority over other methods due to its ability to manage high-dimensional data and its capabilities for both linear and nonlinear separation. While MLP and RBF also provided acceptable results, their accuracy was lower than that of SVM. Employing K-means for dimensionality reduction improved both the speed and accuracy of classification. Conclusion: This study presents, for the first time, an integrated model for high-accuracy diagnosis of oral lesions using imaging. This approach minimizes misdiagnosis errors and reduces the time and costs associated with diagnosis. The application of artificial intelligence in diagnosing oral lesions can enhance healthcare quality as an auxiliary tool alongside dental professionals. This model holds potential for development in other medical fields and can serve as a reference for future research.  },  
Keywords = {Artificial intelligence, Machine learning algorithms, Classification, Feature extraction, Oral lesions.},
volume = {11},
Number = {4}, 
pages = {306-322}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.03},
url = {http://jhbmi.ir/article-1-902-en.html},  
eprint = {http://jhbmi.ir/article-1-902-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Mohammadian, Mohammad and KhazaeeMoghadam, Maryam and Saniei, Elham},  
title = {Automatic Detection and Segmentation of Bone, lung, and soft tissue Based on Computed Tomography Scan Using Deep learning}, 
abstract ={Introduction: In the field of radiation therapy and dosimetry, identifying and segmenting different regions and structures of the body in medical images is crucial for calculating radiation dose distribution for optimization, protecting sensitive organs, and improving treatment planning. In this regard, the application of automatic segmentation algorithms and the advancement of deep learning models in the field of medical image analysis enhance both accuracy and speed. This study aims to evaluate two convolutional neural networks (CNNs) and introduce an effective and efficient model for high-accuracy segmentation of bone, lung, and soft tissue based on CT images. Method: This study utilized deep neural networks based on DeepLabV3+, ResNet-18, and MobileNet-v2 pre-trained architectures as the backbone for segmentation. To preprocess the CT scan images and prepare the input data for the neural network algorithms, 3D Slicer was employed to generate mask images for organs, including soft tissues, bones, and lungs. The development process and fine-tuning of the aforementioned deep learning models were carried out in the MATLAB environment, with accuracy and Intersection over Union (IoU) measured to evaluate the performance of the segmentation algorithms. Results: The results indicated that the accuracy of semantic segmentation of bone for the ResNet-18 and MobileNet-v2 neural networks was 97% and 96%, respectively. For lung and soft tissue segmentation, the accuracy of the aforementioned networks was reported as 96.9% and 96.7% (for lung), and 99.2% and 99% (for soft tissue), respectively. Furthermore, the IoU criterion for semantic segmentation of bone by the ResNet-18 and MobileNet-v2 networks was measured at 85% and 84%, respectively. For lung and soft tissue segmentation, this criterion was 90.8% and 91.2% (for lung), and 99% and 99% (for soft tissue) for both networks, respectively. Conclusion: Various evaluation metrics indicate that the MobileNet-v2 neural network demonstrates superior performance and speed compared to the ResNet-18 network in analyzing CT scan images and segmenting the target tissues.},  
Keywords = {Semantic segmentation, medical images, computed tomography images, convolutional neural networks (CNN)},
volume = {11},
Number = {4}, 
pages = {323-335}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.04},
url = {http://jhbmi.ir/article-1-908-en.html},  
eprint = {http://jhbmi.ir/article-1-908-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Zavar, Matineh and Ghaffari, Hamid Reza and Tabatabaee, Hami},  
title = {Enhancing HIFU Lesion Area Detection through Supervised and Contrastive Self-Supervised Learning with Wavelet-Based Feature Extraction and Hard Negatives (HWCSSL)}, 
abstract ={Introduction: Artificial intelligence (AI) has brought about transformative changes across multiple domains including classification, detection, and prediction through the implementation of sophisticated machine learning algorithms, with the ultimate objective of enhancing quality of life and service delivery. Conventional approaches that rely on manually engineered features encounter significant limitations when applied to complex tasks such as medical diagnosis, particularly in terms of suboptimal feature extraction capabilities and heightened sensitivity to noise interference. The emergence of deep neural networks, with their inherent capacity for automated feature extraction, has revolutionized contemporary data analysis methodologies. The current study leverages this technological advancement for the detection of lesions generated by High-Intensity Focused Ultrasound (HIFU), an innovative therapeutic technique developed for oncological treatment and hemorrhage management. The proposed methodology integrates both supervised and self-supervised learning paradigms to enhance detection accuracy by effectively utilizing both labeled and unlabeled clinical datasets. Method: A key challenge in traditional methods lies in optimal feature extraction and hyperparameter tuning. This research develops an innovative framework integrating supervised and contrastive self-supervised learning. The model processes both RF signals and B-mode images as inputs, simultaneously handling labeled and unlabeled data. Data augmentation techniques, including wavelet transforms and hard negative sampling were employed. Model optimization was achieved through advanced algorithms and fine-tuned hyperparameters to enhance performance in complex scenarios. This approach enabled concurrent analysis of multimodal data, improving diagnostic accuracy. Results: The proposed model demonstrated substantial improvements in HIFU-induced lesion classification performance. Quantitative evaluation metrics, including accuracy, precision, recall, and F1-score, consistently validated the model's efficacy in discriminating between healthy and pathological tissue regions. The multimodal integration of signal processing and image analysis components yielded remarkable enhancements in overall system performance. The incorporation of self-supervised learning algorithms coupled with wavelet transform techniques substantially augmented the model's feature extraction capabilities, resulting in superior diagnostic accuracy compared to conventional approaches. Conclusion: This investigation presents a robust and precise framework for HIFU lesion detection that effectively employs self-supervised learning methodologies. The developed system successfully extracts clinically relevant information from unlabeled datasets while achieving significant improvements in diagnostic reliability. By addressing critical challenges in medical imaging applications, particularly in non-invasive therapeutic interventions, this approach demonstrates considerable potential for enhancing clinical workflows. The proposed methodology establishes a foundational platform for the future development of advanced diagnostic tools in therapeutic ultrasound applications.},  
Keywords = {High-intensity focused ultrasound, Deep neural networks, Self-supervised learning, Lesion detection, Contrastive learning, Wavelet transform},
volume = {11},
Number = {4}, 
pages = {336-347}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.05},
url = {http://jhbmi.ir/article-1-897-en.html},  
eprint = {http://jhbmi.ir/article-1-897-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {Haghdoost, AliAkbar},  
title = {Healthcare System Intelligentization: Benefits and Challenges}, 
abstract ={  The article is a letter to the editor, so it does not have an abstract.},  
Keywords = {Intelligentization, Healthcare System},
volume = {11},
Number = {4}, 
pages = {348-351}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.06},
url = {http://jhbmi.ir/article-1-923-en.html},  
eprint = {http://jhbmi.ir/article-1-923-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}

@article{ 
author = {salehinejad, simin and Bahaeddinbeigi, Kambiz},  
title = {Development and Implementation of an Electronic Health Information System in Disasters and Emergencies}, 
abstract ={Setting up an electronic system to record health data during disaster response is crucial. It improves crisis management quality, lowers casualties, and boosts healthcare service efficiency. Policymaker support and enough funding are essential to developing and maintaining this system. This policy brief outlines the challenges and offers strategic recommendations for building and launching an electronic health data system for disasters and emergencies.    },  
Keywords = {Information Technology, Electronic Data Recording, Disasters, Emergencies},
volume = {11},
Number = {4}, 
pages = {352-358}, 
publisher = {دانشگاه علوم پزشکی کرمان},

doi = {10.34172/jhbmi.2025.07},
url = {http://jhbmi.ir/article-1-931-en.html},  
eprint = {http://jhbmi.ir/article-1-931-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2025}  
}