مجله انفورماتیک سلامت و زیست پزشکی
سال هشتم شماره 3 (پیاپی 29، پاییز 1400)

Many people call emergency medical centers (EMCs) during a day due to accidents or various diseases and they need to receive first aid. In order to provide first aid, it is necessary to send emergency workers and vehicles from the emergency medical services centers to the location of the accidents. Therefore, the accessibility of the EMCs and their being located in an appropriate place are of great importance. The objective of this study was to analyze the spatial distribution pattern of the existing EMCs and to introduce suitable areas for the establishment of new centers.

The present study was a descriptive-analytical one. Ardabil, a city in Iran, was selected as the study area for analysis. A total of 22 criteria were used for site selection. The Analytic Network Process (ANP) was used to determine the weights of the criteria and the VIKOR model was utilized in Geographic Information System (GIS) to prepare an urban zoning map for the site selection of new centers.

The existing EMCs cover only a limited part of Ardabil and many parts of the city, including the outskirts, cannot receive proper emergency medical services (EMS). The created map suggested new locations to increase accessibility.

Spatial redistribution and allocation of EMCs can improve accessibility of these centers. The GIS can provide a good platform for the redistribution of EMS. Health policymakers and planners may use the results of this study to optimally allocate resources.

Self-care in chronic diseases implies the study and control of disease symptoms, maintaining a healthy lifestyle, and daily functioning. The objective of this study was to investigate the effect of educational text messages on self-care in hypertensive patients in one of the hypertension clinics in Kerman in 2020.

The statistical population of this quasi-experimental study included 316 patients with a history of hypertension, of whom 64 were selected as the sample of the study. Then, the selected participants were randomly assigned to two intervention and control groups each with 32 members. The self-care behavior questionnaire for hypertensive patients was used to collect data. Before the intervention, the questionnaires were completed by the participants. Then, educational text messages were sent to the intervention group for one month while the control group received no intervention. At the end of the training, all patients completed the questionnaires again. Data were analyzed using SPSS software (version 26).

The results showed that the mean score of self-care in the intervention group after the intervention (previous score being controlled) was 6.92 more than that of the control group, which was statistically significant (P-value <0.01). Thus, using mobile health (mHealth) had a greater impact on increasing self-care in patients with hypertension compared with the conventional self-care methods.

The results of this study indicated the efficiency of educational text messages in self-care of patients with hypertension. Therefore, it is a good choice to use mHealth technologies for providing health care.

The features of a cell can be extracted from its gene expression profile. If the gene expression profiles of future descendant cells are predicted, the features of the future cells are also predicted. The objective of this study was to design an artificial neural network to predict gene expression profiles of descendant cells that will be generated by division/differentiation of hematopoietic stem cells.

The developed neural network takes the parent hematopoietic stem cell’s gene expression profile as input and generates the gene expression profiles of its future descendant cells. A temporal attention was proposed to encode the main time series and a spatial attention was also provided to encode the secondary time series.

To make an acceptable prediction, the gene expression profiles of at least four initial division/differentiation steps must be known. The designed neural network surpasses the existing neural networks in terms of prediction accuracy and number of correctly predicted division/differentiation steps. The proposed scheme can predict hundreds of division/differentiation steps. The proposed scheme’ error in prediction of 1, 4, 16, 64, and 128 division/differentiation steps was 3.04, 3.76, 5.5, 7.83, and 11.06 percent, respectively.

Based on the gene expression profile of a parent hematopoietic stem cell, the gene expression profiles of its descendants can be predicted for hundreds of division/differentiation steps and if necessary, solutions must be sought to encounter future genetic disorders.

Uncertainty and sudden occurrence of disasters require special attention and nurses, as the largest human resource, have a critical role in maintaining and promoting the health. The use of telenursing care during disaster is an applicable way to provide care. The aim of this was to prioritizing telenursing care in disasters from the view of nurses.

This descriptive-analytical study was part of a larger mixed methods on prioritizing telenursing care in disasters from the view of nurses. Samples were selected using census. The data were analyzed using SPSS software.

The prioritizing extracted care included:(1) Management of trauma, (2) Technical skills, (3) Care and decision-making in stressful situations (triage), (4) Management of patients with special needs, (5) Life-saving intervention, and (6) Psychological and emotional supports.

According to the shortage of specialized nurses in disastrous areas, a telenursing program will provide a new window and provide help to victims of disasters.

The Anterior Cruciate Ligament (ACL) plays a vital role in stabilizing the knee. ACL lesions cause joint instability and severe disturbances in physical function and balance. After ACL reconstruction surgery, patients' ability to perform daily activities is significantly reduced and rehabilitation programs are necessary. The objective of the present study was to use an expert system for virtual reality application with the Wii Fit system to run a rehabilitation program in patients after ACL reconstruction.

This pilot study was performed as a randomized clinical trial on 20 eligible patients in two treatment groups over four weeks; one group with an expert system-based program with Wii Fit (WF group) and the other group with a standard rehabilitation program (CT group). In the first phase of the study, an expert system was developed with the help of specialists, then in the clinical phase, all patients were evaluated and rehabilitated. In statistical analysis, paired t-test and independent t-test were used.

Findings from statistical analysis showed significant differences before and after rehabilitation between the two groups in pain (P<0.001), joint swelling (P<0.001), thigh girth (P<0.001), Knee range of motion (P=0.002), and functional status of patients with Single-leg hop for distance (P<0.001), and Single-leg hop for time (P=0.014). The results revealed a significant improvement in patients in the WF group compared to the CT group.

The rehabilitation approach using the expert system for Wii Fit exercises has had significant effects on pain, swelling, range of motion, and patient performance compared to the usual rehabilitation method.

Lack of financial resources and liquidity are the main problems of hospitals. Pharmacies are one of the sectors that affect the turnover of hospitals and due to lack of forecast for the use and supply of medicines, at the end of the year, encounter over-inventory, large volumes of expired medicines, and sometimes shortage of medicines. Therefore, medicine prediction using available retrospective data leads to improved resource management in hospitals. Due to the high capability of data mining in modeling medical problems, selected algorithms were used to determine the optimal model of medicine preparation.  

In this cross-sectional study, to investigate different types of data mining algorithms, an information form was developed based on the design objectives and then defined in the form of reports in the hospital information system. The data were extracted using Crystal Report software. To develop the model, the accuracy of the data mining prediction algorithms including KNN, SVM, NN, Random Forest, LR, and Adaboost was examined based on MSE, RMSE, MAE, and R2 criteria in Weka software.

Concerning R2, MAE, and RMSE criteria, Adaboost method (0.78, 247, 827) and random forest method (0.6, 1170, 1868) had the highest accuracy compared to other models and reduced the error rate more. Other methods with the above criteria had poorer performance in predicting the research problem.

The results of this study indicated that the Adaboost and random forest methods are more accurate than other methods. A small percentage of hospitals plan to manage the preparation of medicines; thus, it is suggested that managers of hospitals and pharmacies use data mining in the management of their respective units.

Cardiovascular diseases are one of the leading causes of mortality in today’s industrial world. Occlusion of left atrial appendage (LAA) using the manufactured devices is a growing trend. The objective of this study was to develop a computer-aided diagnosis system for the identification of LAA in echocardiographic images.

The data used in this descriptive analytical study included 3D echocardiographic images of the heart of 32 patients in King’s College Hospital in London. All patients were treated successfully using the LAA closure device. A total of 208 two-dimensional images were first obtained from each 3D echocardiographic image data set. Then, 1914 images in which the LAA region was clearly recognizable were selected for this study. The proposed neural network was compiled based on the YOLOv3 algorithm. Finally, 1369 and 545 images were used for training and testing the algorithm, respectively.

The performance of the algorithm in detecting the LAA on a set of 545 images was compared with the regions detected in similar images by an expert in echocardiography through intersection over union (IOU). The algorithm was able to correctly identify the LAA region in all 545 examined images with an average IOU of 99.37%.

The proposed image-based algorithm could detect LAA region in echocardiographic images with a high accuracy. This method can be used to develop algorithms for automatic analysis of the LAA region to determine the size of the closure device and to plan an efficient procedure in LAA occlusion methods.

Various studies have demonstrated the benefits of using distributed fog computing for the Internet of Things (IoT). Fog computing has brought cloud computing capabilities such as computing, storage, and processing closer to IoT nodes. The new model of fog and edge computing, compared to cloud computing, provides less latency for data processing by bringing resources closer to users. This is essential for delay-sensitive applications such as remote healthcare and provides more reliable services. In this study, a fog-based system was proposed to monitor the condition of heart patients.

This study was a developmental-applied one. A set of data relevant to coronary heart patients available in the machine learning data repository of the University of California Irvine was used for evaluation. In this system, each of the heart patient's symptoms is evaluated based on the normal range in the fog layer and the status of the patient is determined. In this layer, requests are prioritized based on the number of symptoms that are out of the normal range. The efficiency of the proposed system was evaluated in terms of network usage time, latency, and response time.

The system presented in this study led to the improvement of network usage time by 23.77%, reduction of latency by 23.71%, and enhancement of response time by 32.95%.

Using the priority queue to prioritize requests at the fog layer reduces the response time to emergency requests.