@article{ 
author = {Eslami, Majid and Mousavian, Iman and EskandariFarsani, Farideh and Dadgostar, Reyhane and Asadollahi, Mohamad and Rahimi, Negar and Hatami, Atefeh and Mohammadkhani, Samaneh},  
title = {Evaluation of a Virtual Reality-Based Wearable Device for Patients with Ankle Movement Disorders}, 
abstract ={Introduction: Rehabilitation is one of the priorities that should be performed on patients with stroke injuries or accidents leading to disability. This study aimed to evaluate the equipment and designed virtual reality environment to improve the treatment of patients with mobility problems in the lower torso (ankle). Method: The research consisted of three basic parts. In the section of mechanics, a movement mechanism was developed after design, and with the help of electronic equipment and Arduino (1.18.5), movements were measured. And finally, by designing a virtual reality environment in Unity software, communication with hardware, processors, and sensors was provided. Results: Paying attention to graphic attractiveness and encouraging users to reuse the virtual reality system is one of the desirable results of this project, which can be effective in motivating users. In addition, considering the existence of distance and time of movement in a virtual reality environment, which depends on the user's movements in the real environment, it is possible to intelligently assess the patient's progress based on the number of sessions and distance traveled. Conclusion: Virtual reality-based rehabilitation methods can have a good effect on the treatment process due to the graphic attractiveness in this environment, and along with other rehabilitation methods, can be effective in faster recovery of people who need rehabilitation services. This method can help patients return to normal living conditions and reduce the time of this process.},  
Keywords = {Virtual Reality, Unity, Motion Sensor, Ankle Rehabilitation, Arduino},
volume = {9},
Number = {1}, 
pages = {1-11}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-683-en.html},  
eprint = {http://jhbmi.ir/article-1-683-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2022}  
}

@article{ 
author = {Shishehchi, Saman and Banihashem, Seyed Yashar},  
title = {Development of a Combined System Based on Data Mining and Semantic Web for the Diagnosis of Autism}, 
abstract ={Introduction: Autism is a nervous system disorder, and since there is no direct diagnosis for it, data mining can help diagnose the disease. Ontology as a backbone of the semantic web, a knowledge database with shareability and reusability, can be a confirmation of the correctness of disease diagnosis systems. This study aimed to provide a system for diagnosing autistic children with a combination of semantic web and data mining. Method: Data is taken from the UCI database. There were a total of 292 data records available of which 80% (234 records) were used for modeling through the decision tree. Knowledge about patients and autism disease was presented via ontology using the Protégé 5 software. The ontology has four classes and 12 properties to communicate between the individuals in the classes. The rules extracted from the decision tree were transformed into a comprehensible form (SWRL) for interpretation in the ontology using a converter. Results: Whether the child is healthy or not can be determined by the rules obtained in the decision tree. In addition, the output of the ontology using the interpretation of 25 rules confirmed the diagnosis of an Autistic child using the decision tree. The evaluation of the ontology also confirmed its correctness. Conclusion: According to the similarity between the result of the ontology and the decision tree regarding the diagnosis of the disease, the accuracy of the proposed method can be emphasized.  },  
Keywords = {Autism, Data Mining, Decision Tree, Ontology, Rule-based System},
volume = {9},
Number = {1}, 
pages = {12-24}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-681-en.html},  
eprint = {http://jhbmi.ir/article-1-681-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2022}  
}

@article{ 
author = {Moulaei, Khadijeh},  
title = {Future studies of physicians\' practice and digital health}, 
abstract ={The article is a letter to the editor, so it has no abstract.},  
Keywords = {},
volume = {9},
Number = {1}, 
pages = {25-26}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-702-en.html},  
eprint = {http://jhbmi.ir/article-1-702-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2022}  
}

@article{ 
author = {Makhloughi, Fatemeh and Goshvarpour, Ateke},  
title = {Prediction of Covid-19 Prevalence and Fatality Rates in Iran Using Long Short-Term Memory Neural Network}, 
abstract ={Introduction: The rapid spread of COVID-19 has become a critical threat to the world. So far, millions of people worldwide have been infected with the disease. The Covid-19 pandemic has had significant effects on various aspects of human life. Currently, prediction of the virus's spread is essential in order to be safe and make necessary arrangements. It can help control the rate of its outbreak and deaths. Previous studies have mainly used statistical tools and machine learning-based algorithms. However, the former was inadequate for analyzing unpredictable epidemics, and the latter experienced under-fitting or over-fitting problems. This research has proposed a method based on deep learning on long-term data to overcome these problems. Method: In this cross-sectional analytical study, we presented an approach for predicting the confirmed and death cases of COVID-19 based on long short-term memory (LSTM) networks. The LSTM model was applied to the time series data of Iran between January 22, 2020, and December 14, 2021, and RMSE and MAE evaluation metrics were calculated. Results: The best results of this study were RMSE = 27.57 and MAE = 19.01 for predicting death cases data. The results showed that the LSTM neural network had a good performance in predicting the number of confirmed and death cases of COVID-19 in Iran. Conclusion: The proposed model showed that it was appropriate for modeling and predicting the prevalence of the virus. Estimating the number of confirmed and death cases of COVID-19 can help control the pandemic situation.},  
Keywords = {COVID-19, Time Series Prediction, Recurrent Neural Network, Long Short-Term Memory, Iran},
volume = {9},
Number = {1}, 
pages = {27-39}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-667-en.html},  
eprint = {http://jhbmi.ir/article-1-667-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2022}  
}

@article{ 
author = {Yousefi, Mahdi and Hedyehzadeh, Mohammadrez},  
title = {Recurrence Detection of Non-small cell Lung Cancer (NSCLC) Tumors Using Imaging and Clinical Features}, 
abstract ={Introduction: Evaluation of treatment response is one of the most challenging tasks in the treatment planning of cancer cases. Regarding the significant effect of tumor recurrence in the treatment planning of patients with lung cancer, finding an approach to predict the recurrence of these tumors is of great importance. Nowadays, this process is done experimentally, and its accuracy depends on the experience and proficiency of the oncologist. This study aimed to provide an automated method to detect the recurrence of lung cancer based on imaging and clinical features. Method: Our proposed method was evaluated in 162 patients with non-small cell lung cancer (NSCLC) using the NSCLC radiogenomic database in the Cancer Imaging Archive (TCIA) portal. After pre-processing, segmentation was performed using the Otsu method. In the next step, the radiomic features were extracted using pre-trained AlexNet and GoogleNet models, and along with clinical features, they were used to detect lesion recurrence. Finally, all cases were classified into two classes using machine learning methods. Results: The proposed method used clinical and deep features. The classification was done using various models, and the accuracy of the support vector machine by AlexNet features resulted in the highest performance. The mean values of accuracy, sensitivity, and specificity for this model are 99.76, 99.77, and 99.76%, respectively. Conclusion: The main finding of this study was revealing the capability of deep learning methods in extracting features from medical images. For example, the AlexNet was able to extract features from CT images of NSCLC patients, which are very helpful in the recurrence prediction of these lesions.},  
Keywords = {Non-small cell Lung Cancer, Image Processing, Machine Learning, Deep Learning, Radiomics Features},
volume = {9},
Number = {1}, 
pages = {40-49}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-692-en.html},  
eprint = {http://jhbmi.ir/article-1-692-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2022}  
}