مجله انفورماتیک سلامت و زیست پزشکی
سال ششم شماره 3 (پیاپی 21، پاییز 1398)

Arthroplasty is a major orthopedic operation with an increasing rate. The success of this operation can significantly reduce patients’ pain and disabilities. This study aimed to design a registry system for hip and knee arthroplasties.

A comprehensive search was conducted to retrieve minimum data set from articles, guidelines, forms and reports published by orthopedic societies. Then, orthopedists were interviewed and medical records were evaluated for system requirements. After thematic analysis of the qualitative data, the intended system’ requirements were extracted. A system was designed following the "Information System Development Life Cycle and Object-Oriented" approach. The system prototype was developed by Python programming language and PostgreSQL Data Base Management System. Then, the usability of the system and user satisfaction were tested. Quantitative data were analyzed using descriptive statistics and through thematic and quantitative approaches.

The required dataset and processes were extracted based on evaluating nine arthroplasty registries of pioneer countries as well as our local needs and requirements. The result was a minimum dataset comprising of 39 elements in 5 groups. They were used for developing the arthroplasty registry forms for hip and knee. The system was considered applicable and useful by potential users.

An arthroplasty registry system was developed successfully. This system can provide a ground base for healthcare policymakers as well as the members of orthopedic society for planning a good quality care for arthroplasties.

Gait analysis through using modern technology for detection of Alzheimer's disease has found special attention by researchers over the last decade. In this study, skeletal data recorded with a Kinect camera, were used to analyze gait for the purpose of detecting Alzheimer's disease in elders.

In this applied-developmental experimental study, using a Kinect camera, data were collected for 12 elderly women with Alzheimer's disease and 12 healthy elderly women walking in an oval path. After extracting various features of gait, descriptive analysis was performed to compare the features between the healthy and patient groups. Then, a support vector machine classifier was designed to detect elderly people with Alzheimer's disease.

The comparison of extracted features from skeletal data of gait using Kinect camera in this study indicate that the results are matched with previous findings from systems based on other types of sensors. The accuracy, sensitivity, precision and specificity of system designed in the present study for classifying elders with Alzheimer's disease and healthy elders were 91.25%, 93.4484%, 90.5945% and 93.581% respectively.

In addition to descriptive analysis of gait, by using machine learning methods such as support vector machine classifier, elderly people with Alzheimer's disease can be detected based on features extracted from skeletal data of Elderly people.

E-mental health interventions can be effective in the early diagnosis and treatment of mental problems such as depression. Therefore, the present study aimed to evaluate the awareness and practice of health care providers on E-mental health interventions and its’ utilization barriers.

This descriptive study was conducted in Kerman in 2018. The research population consisted of family physicians, psychologists and health care providers in health care centers who were selected through systematic random sampling. Data collection tool was a valid and reliable researcher-made questionnaire consisting of two parts of demographic questions and items related to awareness, usage, and barriers of using E-mental health interventions. Spearman, Kendall, and Chi square tests were used to analyze the data.

A total number of 143 people participated in this study of whom, 69% had not used E-mental health interventions so far and only 9% had been trained in these interventions. In regard to E-mental health interventions, psychological training (24%) and peers’ support by using online discussion forums (17%) gained the highest rates of use among participants and in regard to barriers of implementing and using these interventions, clinical risks (97%) and unwillingness of patient and physician in using electronic interventions (88%) gained the highest rates.

In general, health care providers’ awareness and practice on E-mental health interventions were low. Therefore, identifying and prioritizing these barriers can help to enhance the capabilities of these technologies and to promote their use.

Opposition and hyperactivity are among behaviors of children with Oppositional Defiant Disorder (ODD). Nowadays, due to the changes in life style and busy life, direct referring to health provider is difficult. Therefore, the present study was done to examine the effectiveness of training behavior management flowchart by mobile to mothers for reducing oppositional and hyperactivity symptoms in children with oppositional defiant disorder.

This research was done in Tehran/ Iran in 2018 and through single case experiment by A-B model. Four mothers whose children had gained high scores of ODD were selected for training in eighth sessions of 60-90 minutes. Mothers received the training program of behavior management for six virtual sessions through mobile phone and two in person sessions. Data were analyzed using chart, improve percent and RCI.

The overall improvement was 61.43% for defiant and 69.74% for hyperactivity. In one-month follow up, both mentioned variables improved (75.77% and 79.47% respectively). According to the results, the two hypotheses were clinically significant.

The mobile health technology is a low cost, available, and suitable method for mental health services and can be used as a complementary health service for mothers who have a child with oppositional defiant disorder.

Diabetic retinopathy is the damaging effect of diabetes on retinal blood vessels that can cause blindness when diagnosed late. Microaneurysms are early signs of the disease that their early diagnosis promotes timely treatment and prevents disease progression. Since this disease is asymptomatic and can only be detected by ophthalmologists, diabetic patients should be tested regularly. On the other hand, given the fact that the increase rate of the number of ophthalmologists is less than the growth of the diabetic population, manual diagnosis of the lesion is time consuming and costly, and thus the design of automatic detection systems is essential.

In this descriptive analytic study, the fundus images of the retina were subjected to preprocessing. Then, the candidate regions of microanurysms were determined using the metric and morphological operators Bottom-hat and Hit-or-Miss. In the next step, using principal component analysis, the specificity of main feature of real microanurysms diagnosis was extracted. The DiaRetDB1 database images were used to evaluate the proposed algorithm.

The purpose of this research was to develop an automated method for the detection of microanurysms that can help ophthalmologists in the process of diabetic retinopathy screening and diagnosing the symptoms faster, easier and at lower cost. In evaluation, the proposed method achieved a sensitivity of 87.6%, specificity of 98.7% and the precision of 85.7%.

According to the results obtained based on evaluation parameters, the proposed method is one of the most accurate algorithms in this field.

The aim of this study was to determine the status of ICD-10 codes assigned to cancer patients' medical records in terms of three attributes of accuracy, completeness, and timeliness.

in this cross-sectional descriptive study, 374 medical files with C00-D48 diagnosis codes were selected through stratified sampling. Data gathering tool was a researcher-made checklist consisted of clinical information summary, codes assigned by assistant professors and coders, the review and comparison of the codes, the results obtained from the control of codes by ICD-10 and the errors extracted from coders’ codes. The factors affecting the occurrence of coding errors at different levels and their impact on the accuracy and completeness of the codes were classified.

Totally, coding errors were observed in 79 cases (21.74%). differences in codes at the level of the chapter were due to not following the rules for choosing the main diagnosis (30 cases) and inadequate study of the records (38 cases). In terms of completeness, in 27 cases (7.43%), codes assigned by coders were defective compared to assistant professors’ codes. In relation to the timeliness, in accordance with the WHO standard, coding was done within 48 hours of the patient's discharge.

According to the results, coding quality is not only dependent on the coders, but also on other factors such as documentation defect. Therefore, continuous training for both coders and documenters is necessary to resolve defects, especially in the field of cancer. It is also recommended to use the results of this study for planning related workshops.

Nowadays, the effects of radiation and constant use of cell phones have led to some problems. These radiations cause disorders in different systems of human body and even in a growing fetus. The aim of this study was to find the effect of using cell phone and internet by pregnant women on the amount of amniotic fluid.

First, a questionnaire was designed and evaluated by obstetricians and telecommunication specialists. The questionnaire includes questions regarding the amount of using cell phone, internet and Wi-Fi during a day. After data collection and preprocessing with predictive Apriori algorithm, the effective factors on the amount of amniotic fluid were investigated and proper associative rules were extracted. The next phase is dedicated to predicting the risks of decreased amniotic fluid by classification algorithms.

Early awareness of doctors as well as the patient can cause avoiding this problem through some related life style changes by mothers. SVM algorithm was successful in arriving at the best results with 80% accuracy.

The most important rule showed that more than 6 hours cell phone conversation, more than 4 hours internet use and more than 8 hours Wi-Fi use by pregnant women can definitely decrease the amount of amniotic fluid. Also, the proposed method, using different classification algorithms, can predict the risk of decreasing the amount of amniotic fluid in pregnant mothers.

Human Serum Albumin (HSA) is one of the most important proteins in blood that can bind a wide range of components and different drugs such as Warfarin and is also circulated in the body by HSA. Therefore, studying HSA is very significant in pharmacology. In this research, dynamic behavior of residues of Warfain binding site of HSA has been investigated.

Firstly, PDB format of HSA was downloaded from RCSB (PDB ID:2BX8) which was in complex with Azapropazone. After that, molecular dynamics simulation was during 30ns by GROMACS package. CHIMERA software was used to discover residues of binding site.

After RMSD analysis was done on residues of binding site, it was seen that Arginines 186 and 218 have a wide fluctuations in their RMSD plot. Also, two Lysines 185 & 190 have nearly wide fluctuations of RMSD, this fluctuations were less than Arginine's, however. Other residues such as Glycine 189 have few fluctuations. Fluctuations in RMSD have direct relationship with accessible surface area (ASA) of the residues.

The results show that the Warfarin binding site in HSA can have various conformational situations because of the dynamics of its residues.  This problem can be important to drug design. Overall, according to the results in this study, residues have been divided into 3 categories. Among them, Tryptophan 214 according to different articles is one of the most important residues site and based on our result, it stays on category of small mobility.