Journal of Health and Biomedical Informatics
Year:2019 | Volume:6 | Issue:1|Papers Count:7

Introduction: Bed bugs are considered as public health nuisance insects, which can feed on humans and cause psychological distress, insomnia, anxiety, anemia, and skin itching in individuals. The aim of this study was to design and implement a mobile application "identification, prevention, and control of bed bug", and also to assess the satisfaction of mobile users with this application. Method: In the first phase of this study, the mobile application that includes three steps of identification, prevention, and control of bed bug, was designed and installed on the people's mobile phone. In the second phase, a descriptive cross-sectional study was conducted on 100 mobile users using an electronic questionnaire. Descriptive data were analyzed by SPSS version 18. Results: The satisfaction rate of the application users in most of the questions was at high level (more than 80%). According to the users' answers to the question about the impact of educational data presented through this application, its impact was high (78%). Conclusion: Due to high accessibility, this application increases individuals' knowledge, saves cost and time to control bed bugs, therefore, the use of this mobile health application is recommended.

Introduction: Clinical Decision Support Systems (CDSS) are designed in the form of computer programs that help medical professionals make decisions about disease diagnosis. The main aim of these systems is to assist physicians in diagnosing diseases, in other words, a physician can interact with the system and use them to analyze patient data, diagnose diseases, and other medical activities. Method: This is a descriptive-analytic study. The datasets include 768 records of diabetes with 8 features and 155 records of hepatitis with 19 features, which were provided by the Global Website of UCI. In this study, the Particle Swarm Optimization (PSO) algorithm was used for Feature Selection (FS) and the Firefly Algorithm (FA) was used to classify diabetes and hepatitis into two healthy and unhealthy classes. 80% of the data was used for training and the remaining (20%) was used for testing. Results: The experiments showed that the accuracy of the PSO and FA for the diabetes dataset was 84. 41% and 82. 08%, respectively. Also, the accuracy of the PSO and FA for the hepatitis dataset was 81. 84% and 80. 34%, respectively. The accuracy of the proposed model for the diabetes and hepatitis datasets was 95. 38% and 94. 09%, respectively. Conclusion: According to the results, the proposed model had a lower error rate in diagnosis compared to the PSO and FA. The results of this study can help doctors in timely diagnosis of diabetes and hepatitis.

Introduction: Considering the importance of the evaluation and identification of BDNF protective pathways, this study was conducted to analyze the expression rate of genes registered in the NCBI database to identify the genes expressed in SH-SY5Y cell line due to BDNF protection and oxidative stress and also to identify the protective pathways of BDNF. Method: In this study, bioinformatics and NCBI databases and libraries including 48000 datasets were explored and data were collected using Illumina and Bid studio software. In the first phase, sampling was performed manually based on the P-value, and in the second phase, based on the relationship with compatibility of neurons, the Pearson correlation coefficient, Spearman's correlation coefficient, and linear relationship were calculated by measuring fit or regression using SPSS version 20. Results: The Pearson correlation between CMP and CTR data was positive; and the linear regression between them was also positive. The frequency percentage of neuron adapter proteins obtained from CTR data was higher than that from CMP data; and a great number of protective proteins were related to the protection of cell shape and cellular skeleton, and neuron survival. Conclusion: Due to the contact of neurons with BDNF, some genes are specifically expressed, therefore, BDNF can increase the life and compatibility of neurons.

Introduction: Picture Archiving and Communication System (PACS) allows the processing, archiving, and sharing medical images electronically with different parts of the hospital, especially the emergency department. The aim of this study was to evaluate the effect of PACS on the diagnosis accuracy of emergency department physicians before and after its implementation. Method: In this analytical study, the diagnosis of emergency physicians was compared with that of radiologists in each period. Eventually, the data obtained in the two periods were compared. In this study, 380 and 509 CT scans were analyzed before and after the PACS implementation, respectively. Data analysis and comparison of the accuracy and agreement of diagnosis in the pre-and post-PACS implementation periods were performed using Chi-square test, and statistical significance level was calculated using SPSS version 24. Results: The accurate diagnosis of CT scan examinations increased from 284 examinations (75. 9%) before PACS implementation to 428 examinations (84. 4%). The diagnostic agreement also increased from 306 examinations (81. 8%) before PACS implementation to 452 examinations (89. 2%). Statistical significant level was considered at P<0. 05. Conclusion: The accuracy and agreement of the diagnosis of emergency physicians in CT scan examinations in the post-PACS implementation period increased compared to the pre-PACS implementation period; as a result, the implementation of PACS increases the diagnostic agreement between emergency physicians and radiologists, followed by an increased diagnosis accuracy of emergency physicians.

Introduction: Diabetes or diabetes mellitus is a metabolic disorder in body when the body does not produce insulin, and produced insulin cannot function normally. The presence of various signs and symptoms of this disease makes it difficult for doctors to diagnose. Data mining allows analysis of patients’ clinical data for medical decision making. The aim of this study was to provide a model for increasing the accuracy of diabetes prediction. Method: In this study, the medical records of 1151 patients with diabetes were studied, with 19 features. Patients’ information were collected from the UCI standard database. Each patient has been followed for at least one year. Genetic Algorithm (GA) and the nearest neighbor algorithm were used to provide diabetes prediction model. Results: It was revealed that the prediction accuracy of the proposed model equals 0. 76. Also, for the methods of Naï ve Bayes, Multi-layer perceptron (MLP) neural network, and support vector machine (SVM), the prediction accuracy was 0. 62, 0. 65, and 0. 75, respectively. Conclusion: In predicting diabetes, the proposed model has the lowest error rate and the highest accuracy compared to the other models. Naï ve Bayes method has the highest error rate and the lowest accuracy.

Introduction: In protein-protein interaction networks (PPINs), a complex is a group of proteins that allows a biological process to take place. The correct identification of complexes can help better understanding of the function of cells used for therapeutic purposes, such as drug discoveries. One of the common methods for identifying complexes in the PPINs is clustering, but this study aimed to identify a new method for more accurate identification of complexes. Method: In this study, Yeast and Human PPINs were investigated. The Yeast datasets, called DIP, MIPS, and Krogan, contain 4930 nodes and 17201 interactions, 4564 nodes and 15175 interactions, and 2675 nodes and 7084 interactions, respectively. The Human dataset contains 37437 interactions. The proposed and well-known methods have been implemented on datasets to identify protein complexes. Predicted complexes were compared with the CYC2008 and CORUM benchmark datasets. The evaluation criteria showed that the proposed method predicts PPINs with higher efficiency. Results: In this study, a new method of the core-attachment methods was used to detect protein complexes enjoying high efficiency in the detection. The more precise the detection method is, the more correct we can identify the proteins involved in biological process. According to the evaluation criteria, the proposed method showed a significant improvement in the detection method compared to the other methods. Conclusion: According to the results, the proposed method can identify a sufficient number of protein complexes, among the highest biological significance in functional cooperation with proteins.

Introduction: In order to adopt the right technologies, policy makers should have adequate information about the present and future advances. This study aimed to review future studies in the field of health information technology. Method: This review study was conducted in 2015. The databases including Scopus, Web of Science, ProQuest, Ovid Medline, and PubMed were sought between 2000 and 2015. Results: 11 papers were selected for the study. The papers were divided into two groups: forecasting the future of health information technology (n=7) and health information technology foresight (n=4). According to the results, it is better to use foresight approach for big and long-term goals. Conclusion: The results of foresight studies can be useful for making decision and policy-making in the field of health information technology, particularly at the national level.