Journal of Health and Biomedical Informatics
Year:2020 | Volume:7 | Issue:3|Papers Count:12

Introduction: The American Medical Association has introduced the newest medical subspecialty field known as "Clinical Informatics" since 2011. In order to present similar courses in Iran, it is necessary to design and evaluate a training program. The comparison of educational systems improves the content and quality of design of an educational program. Therefore, the objective of this study was to identify and compare the clinical informatics curriculum in several top universities in the world. Method: The present study was an applied one conducted in 2019. The tool used in this study was a researcher-made checklist that was designed based on reviewing the texts and opinions of experts in group discussion sessions. Its validity was confirmed by seven experts in medical informatics, clinical specialties, and medical education and its reliability was confirmed using the equivalence method. The data were collected by referring to the university websites to complete the study checklist. Results: In most of the studied universities, the clinical informatics fellowship course was implemented according to ACGME standards and the main content of this course included four main topics: principles and concepts, clinical decision-making and improving the care process, health information systems, and guiding and managing change. Conclusion: The similarities were mostly related to the duration of the course and the opportunity to conduct relevant researches in the field of clinical informatics. Most of the differences were also related to the opportunity to teach clinical informatics and learners' participation in holding a journal club during the course.

Introduction: Autism is one of the most common neurodevelopmental disorders that is diagnosed late due to parents' unfamiliarity with the disorder. Lack of knowledge of parents is one of the major problems of this disorder. The objective of this study was to develop an educational smartphonebased application for autism spectrum disorder specific for parents. Method: This study was an applied-developmental one conducted in three phases. First, a questionnaire designed for assessing information needs as well as determining data elements and application features was completed by specialists in the fields of pediatric psychology and pediatric neurology in hospitals affiliated to Iran University of Medical Sciences. The data were analyzed and the application was designed based on the results and finally, in order to evaluate the usability and user satisfaction, it was presented to 34 parents. Results: The majority of the data elements were regarded as necessary by specialists. The main features of the application included training the signs and symptoms of autism, self-care approaches, and diagnostic screening test for the ages of 6, 12, 24, and 36 months. Usability evaluation showed that parents evaluated the application as good with the mean score of 7. 6 (out of 9). Conclusion: Educational mobile application for autism specific for parents, in terms of diagnosis, self-care, and rehabilitation, could be helpful for early diagnosis and treatment of autism and accessible for training parents. Training diagnosis, self-care, and interaction with the autistic child was approved as an accessible tool by parents.

Introduction: Implementing a method that can help individuals diagnose or prevent mental disorders can be an important step in preventing and controlling these disorders especially in the early stages. The objective of this research was to apply data mining techniques for intelligent diagnosis of severity of depressive disorder. Method: The present applied research was carried out by going to a number of psychiatric clinics in Tehran and investigating patients' medical records. A total of 420 subjects who responded to the Minnesota Multiphasic Personality Inventory (MMPI, 71 questions) were selected through convenience sampling as the sample of the study (300 subjects were diagnosed with different degrees of depression and 120 subjects showed no symptoms of depression). The answer sheet of MMPI and the diagnosis of the psychiatrist were used as data for developing the model by k-Nearest Neighbor (k-NN), Decision Tree, and Support Vector Machine algorithms. About 70 percent of the data were applied for training and 30 percent of the data were used for validating the model. MATLAB software was used for data analysis. Results: The results of the evaluations showed that Decision Tree algorithm with accuracy of 99. 16% had higher accuracy compared to other algorithms. Furthermore, by implementing the developed models on each question of MMPI, the influence of each question on evaluation was determined. Conclusion: Classifying patients with data mining approach and based on the most important characteristics can be a useful and effective tool for analyzing and improving the decision-making process of physicians regarding the treatment of patients.

Introduction: One way to evaluate visual-spatial functions is to use line bisection test. With the advancement of information technology, this study attempted to design a line bisection test software and investigate its efficacy compared to the conventional paper-pencil form in schizophrenic patients. Method: The present study was a causal-comparative one and line bisection test software was designed with visual studio 2015 exactly the same as the paper-pencil form. In the first stage, 30 healthy individuals were selected by purposive method according to the inclusion criteria and each of them took 3 software tests and 3 paper-pencil tests and the mean deviations were calculated from the midpoint of the line. Then, the results of the two tests were compared. In the next stage, to test visual-spatial performance, 15 schizophrenic patients were randomly selected from hospitalized psychiatric patients and both software and paper-pencil forms of line bisection test were performed for each patient. Results: Using one-way analysis of variance, the mean deviation from the mean in healthy individuals and schizophrenic patients was evaluated and the results showed no significant difference between software and paper-pencil forms (P<0. 05). Due to the high accuracy of software implementation, the errors due to randomized responses are detectable by the software. Conclusion: The results of this study indicated that line bisection test software is a good alternative to paper pencil test to evaluate the spatial-visual performance of schizophrenic patients and this software can be used to evaluate cognitive functions, especially the parietal lobe injuries of schizophrenic patients.

Introduction: Today, the use of mobile applications to help self-care in patients with chronic diseases has increased. The objective of this study was to investigate the quality of Persian mobile applications related to patients with diabetes and hypertension. Method: This analytical study was conducted on all Persian mobile applications related to diabetes and hypertension in 2019. The mobile applications were searched in the Café Bazaar app store using the keywords such as “ diabetes” , “ blood sugar” and “ blood pressure” . The APPLICATIONs Scoring System was used in this study. Two evaluators assessed the applications and in cases of disagreement a third evaluator was consulted. Results: In total, 179 Persian mobile applications were investigated. The mean and standard deviation of the quality of applications related to diabetes and hypertension based on the applied tools were 7. 31± 1. 49 and 7. 22± 1. 24 out of 16, respectively. More than 75% of these applications were comprehensive and less than 7% used authentic scientific resources. More than 80% of these applications were user-friendly and could work offline. Conclusion: The results of this study showed that most of the Persian mobile applications related to diabetes and hypertension were at a moderate level. Most of the mobile application developers took into account the interactive aspects of applications but paid less attention to providing a scientific content. Evaluating the quality of health-related mobile applications is recommended to app store managers before low-quality applications are available to public.

Introduction: Recently, the Internet of Things (IoT) allows patients and healthcare providers to transfer the treatment process to the patients and enables them to manage the disease and receive help from the healthcare team and mobile devices. This has been considered as a promising solution to improve the quality of healthcare. The objective of this study was to investigate the factors affecting the acceptance of smart healthcare devices using an integrated technology acceptance model. Method: In this descriptive-survey research, 169 out of 300 patients of Gil hospital in Rasht were selected based on Morgan table. The questionnaires by Papa et al. and Li et al. were applied to test the hypothesis. The structural equation technique and t-test were used to analyze the data. Results: According to the research findings, the effect of “ permeability” , “ comfort” , “ perceived social risk” , and “ performance risk” on “ perceived usefulness and ease” , the effect of “ perceived usefulness and ease” on “ attitude towards the acceptance of smart healthcare devices” , and finally, the effect of “ attitudes toward the acceptance of smart healthcare devices” on the “ behavioral intention to use these devices” were confirmed. Conclusion: The results showed that the integrated technology model can play an effective role in identifying and examining the factors affecting the acceptance of smart healthcare devices and can be utilized as an important source of information for the use of these devices.

Introduction: Occupational therapy and performing specific motor activities are among the healing processes for injured people that should be followed by patients in need after the doctor’ s prescription. The objective of this study was to evaluate the effect of using virtual reality environments and interacting with hardware designed for the treatment and rehabilitation of patients with upper limb injuries. Method: In this study, the design and manufacturing process consisted of two parts: software and hardware. In the software section, in order to access the virtual reality environment under the Windows operating system, the virtual reality environment was designed and coded and then the connection to the hardware and microcontroller section of the Arduino board was provided so that commands could be exported from the controller to the virtual reality environment and depending on the mouse motion, a movement in the virtual environment would be made. Results: Given the nature of this research, the criteria proposed by Martilla and James were used to evaluate the software. Moreover, Numeric Pain Rating Scale (NRS) was used to measure the effect of this method on reducing pain and the performance of virtual reality environment in reducing pain was evaluated. Conclusion: The results showed that using virtual reality for rehabilitation, with accordance to the type of graphics provided and the hardware designed, in addition to reducing treatment time and pain level, encourages the patients to use the software and continue the activities in a place other than the clinic.

Introduction: Osteoporosis is one of the major causes of disability and death in elderly people. The objective of this study was to determine the factors affecting the incidence of osteoporosis and provide a predictive model to accelerate diagnosis and reduce costs. Method: In this fundamental descriptive study, a new model was proposed to identify the factors affecting osteoporosis. Data related to 4083 women were investigated with Clementine12, the data mining tool, to discover knowledge. Using data mining algorithms, including decision tree and artificial neural network, some rules were extracted that can be used as a model to predict the condition of patients and finally, the accuracy of the proposed models were compared. Results: This study examined several models on a number of different characteristics and compared the results in terms of accuracy to find the best predictive model. The classification accuracy of the MLP neural network model was 92. 14% which was higher than that of the other algorithms used in this study. According to the identification of factors affecting osteoporosis, the risk of developing this disease can be predicted for a new sample. Conclusion: Healthcare organizations are always gathering a lot of information while this data is not used properly. This study showed that the hidden patterns and relationships in this data can be discovered and used to improve the quality of diagnostic and treatment services.

Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals which is known as ECG. The ECG signals are accompanied by noise due to external sources or other physiological processes in the human body. Method: In this applied research, an adaptive filter based on wavelet transform and deep neural network was proposed to reduce the noise. The proposed method was a combination of wavelet transform, adaptive learning, and nonlinear mapping of deep neural networks. Deep neural network was used with an adaptive filter to reduce more noise in the ECG signal. Results: Signal-to-Noise ratio (SNR) was used as a criterion to evaluate the quality of the proposed method to remove noise. In fact, the objective of this research was to increase this ratio which indicates higher efficiency of the method based on wavelet transform and deep learning. Conclusion: The results of the simulation showed that the proposed method improved the removal of noise from the ECG signal about 9. 56% compared to existing methods. The reason is that the coefficients extracted from adaptive filter were optimized using deep neural network so that it provided a low-noise waveform.

Introduction: FAD is the cofactor of FAD-FR protein family. Sulfite reductase flavoprotein alphacomponent is one of the main enzymes of this family. Based on applications of this enzyme in biotechnology and industry, it was chosen as the subject of evolutionary studies in 19 specific species. Method: Gene and protein sequences of sulfite reductase flavoprotein alpha-component, 5S rRNA sequences, and taxonomic tree were extracted from 19 selected bacterial species. Then, phylogenetic trees of 5S rRNA and gene and protein sequences were compared with each other and with taxonomic tree. Phylogenetic trees were drawn by Mega7 software using neighbor-joining algorithm and taxonomic tree was extracted using NCBI taxonomy browser. Results: By comparing the corresponding tree pairs, the percentage of equivalent species and the mean equivalence score of species were calculated for each tree pair. The gene-protein tree was allocated the highest scores in both quantities. In comparing the taxonomic tree with three other trees, gene-taxonomy tree achieved the highest percentage in the mean equivalence score and protein-taxonomy tree obtained the highest percentage of equivalent species. Conclusion: Based on the results of the present research, the best replacement for each of the trees investigated in this study regarding evolutionary relations was identified. In other words, this study helps detect which evolutionary tree can be replaced for another evolutionary tree.

Introduction: Wearable electronic devices, which are based on Internet of Things (IoT) and big data computing, are able to continuously collect and process the physiological and environmental data and exchange them with other tools, users, and internet networks. Therefore, despite their potential benefits in health monitoring, they can pose serious risks, especially in breach of privacy. Hence, the main question in this study was to identify the most important strengths, weaknesses, opportunities, and threats related to wearable electronic technologies. Method: In this study, StArt 3. 4 software was used for systematic review. Studies until November 30, 2019 were searched for keywords in “ Scopus” , “ IEEE” , “ PubMed” , “ Springer” , “ Magiran” , “ SID” , and “ Sivilica” databases and Google search engine. Results: After deleting duplicate and unrelated documents, 80 documents were selected for final review and were analyzed using descriptive statistics. Accordingly, the main identified strength, weakness, opportunity, and threat were “ improving lifestyle and human capabilities” , “ low data reliability and user interface” , “ applications in health and medicine” , and “ information abuse and privacy breach” with 97. 5%, 92. 5%, 94%, and 99% frequency, respectively. Conclusion: The results of this study showed that improving human capabilities and application in medicine and health care are the main driving forces for the development of wearable electronic technologies. Therefore, in order to take advantage of the opportunities and overcome the potential threats of this technology, planning for the development and application of indigenous knowledge, as well as the development of the required standards and rules, must be put on the agenda immediately.