مجله انفورماتیک سلامت و زیست پزشکی
سال ششم شماره 4 (پیاپی 22، زمستان 1398)

Liver cirrhosis has grown significantly in Asian countries over the last few years. However, there is no tool for collecting liver cirrhosis data. The purpose of this study was to design a minimum data set for the self-care education of liver cirrhosis.

This descriptive study was conducted in the first half of 2018 in cooperation with 16 liver specialists of Rasool Akram Hospital and Firoozgar Hospital in Tehran. The research tool was a researcher-made questionnaire. The content validity method was used to validate the questionnaire and its reliability was confirmed through test-retest method (r = 0.84). Data were analyzed using SPSS21 software.

The minimum data set was organized in six categories: demographic, clinical, disease symptoms, dietary recommendations, general recommendations, medications and reminders. The demographic data collection consisted of 15 data elements, clinical data included 27 data elements, clinical symptoms included 9 data elements, food recommendations, consisted of 7 parts and 14 data elements, general recommendations consisted of 7 parts and 12 elements of data, and medications and reminders had two parts and 12 elements.

Considering the importance of data and information in the self-care of patients with liver cirrhosis and the role of software in collecting and sharing data with physicians, as well as reducing the number of visits to the hospital, attention should be paid to the minimum data set of self-care in patients with liver cirrhosis.

Breast cancer is a cancer that is caused by abnormal growth of breast cells. Modeling  and simulation of the growth and treatment of breast cancer, along with providing the possibility of doing experiments and research, can reduce the time and cost of treatment by predicting some cases. The purpose of the present research was to develop an agent-based model for the simulation of breast tissue and the growth and spread of tumor in various breast cancer states.

 In this research, agent-based modeling and simulation method, as well as library studies were used. NetLogo software was used for designing and modeling of breast anatomy and development of model with the required knowledge. Then, according to the patient's age, tumor grading and cancer cells spread, simulation of the growth and development of various breast cancer states in different times were investigated. In order to validate the model, clinical data and reports related to the age and grade, size, growth and spread of tumor in a number of patients with breast cancer were studied and considered and compared with model outcomes.

 According to the experiments related to the analysis and validation of the model, the developed model had relatively acceptable results about the growth of breast tumors and the proliferation of cancerous cells in lymph nodes in various breast cancer states. 

 Agent-based modeling can be used as a relatively useful and efficient method in identifying and analyzing breast cancer behavior and predicting the growth and proliferation of cancer cells.

Poor User Interface design can be one of the probable reasons of users’ error and reduced incentive in using health information systems such as PACS. Heuristic evaluation is one the methods for assuring the proper user interface design of health information systems such as PACS.

This study was a descriptive-analytic research conducted in 2019 through using Nielsen usability checklist. The study setting was Zanjan University of Medical Sciences and data were analyzed in SPSS 25 using descriptive statistics.

In whole, 130 problems were found in 293 usability cases. The most common usability problems include privacy (66.66%), Visibility of System Status (65.52%), and Flexibility and Minimalist Design (60%) and the lowest number of UI design problems were associated with the Pleasurable and Respectful Interaction with the User (14.29%).

Usability Evaluation can identify the reasons of problems that may cause medical errors or users' resistance in working with the system. Therefore, it is necessary for system developers whose systems are widely used in important centers to pay more attention to all standards in designing the system and to prevent the occurrence of errors.

Brucellosis is considered as one of the most important common infectious diseases between humans and animals. Considering the endemic nature of brucellosis and the existence of numerous reports of human and animal cases of brucellosis in Iran, the incidence of human brucellosis in Rafsanjan city was determined in the last 3 years (2016–2018). The main objective of this study was to find an automated consistent and intelligent method with low sensitivity based on a neural network which is capable of accurate detection of brucellosis disease.

In this descriptive analytic study, cases of human brucellosis in Rafsanjan, south of Iran, were analyzed based on sex, age, pregnancy, history of contact with livestock and the use of non-pasteurized dairy products, Right Laboratory parameters and 2ME during 3 years (2016–2018). Data were split into two subsets of train (80%) and test (20%). The artificial neural network approach of the deep auto-encoder was used to train each subset.

The deep auto-encoder method achieves 94.61% sensitivity, 90.84% accuracy and 50% specificity in the diagnosis of brucellosis over the experimental data sets. The experimental results also showed the excellent performance of the proposed artificial neural network.

The deep artificial neural network model can be used as an efficient and intelligent method to detect the human cases of brucellosis. However, further studies are required to design other models of artificial neural networks based on deep learning to detect other infectious diseases.

Breast cancer is one of the most common types of cancer and the most common type of malignancy in women, which has been growing in recent years. Patients with this disease have a chance of recurrence. Many factors reduce or increase this probability. Data mining is one of the methods used to detect or anticipate cancers, and one of its most common uses is to predict the recurrence of breast cancer.

Out of 699 patients with breast cancer, 458 (66%) of them did not relapse and 241 (34%) of their cancer recurred. This information was collected from 91 to 94 years of history of patients with breast cancer in the academic Jihad. In this study, the combination of two nearest neighboring algorithms and a genetic algorithm are proposed to predict the relapse of patients with breast cancer. First, the nearest neighboring algorithm is presented to predict the recurrence of breast cancer. Then, using the genetic algorithm, the dependent variables are reduced to make the model more appropriate.

The number of dependent variables is 14 variables, which is reduced by 6 genetic algorithms to better predict the model. To evaluate the model, the health parameter is used, which is 77.14% for the proposed model, which could not be more suitable for other methods.

In this study, the proposed algorithm was examined with other predictive methods and it was determined that the proposed algorithm is better.

Metastasis is one the most important causes of mortality in cancer patients. Recent studies have shown the metastatic potential of a specific group of microRNAs called metastamirs.  miR-126 is shown to be correlated with the colorectal liver metastasis. Also, overexpression of miR-10b has been reported in metastatic breast cancer.  Therefore, down regulation of these miRNAs at transcriptional level can reduce the probability of metastasis. This study analyzes targeting of miRNAs precursors using CRISPR-C2c2 (Cas13a) technique.

To conduct this study, we used bioinformatics and structural bioinformatics methods. The structure of C2c2 (Cas13a) enzyme was obtained from RCSB database, and the sequences of miRNAs and their precursors were collected from MirBase and Mirnamap. The crRNAs were designed, evaluated and checked for their specificity by using CRISPR-RT. The modeling of the three-dimensional structure of the designed crRNAs was performed by RNAbuilder 2.8.2 software. We used Hdock server to perform molecular docking to assess the energy level and the position of docked molecules. 

The crRNA designed for mir-126 showed high structural similarity with the situation observed in nature and appropriate orientation was obtained. In the case of crRNA designed to target mir-10b, despite high specificity, the correct orientation was not established.

Sequence-based evaluation of crRNAs designed for RNA-level editing is insufficient, and it is recommended that, along with specificity, simulation and molecular docking studies, be performed for higher accuracy.

Drug-drug interactions (DDIs) are a common source of preventable medical errors in inpatient and outpatient settings of both developed and developing countries. When two or more drugs are simultaneously prescribed, interactions between their effects may result in preventable adverse events such as damages to vital organs, frequent hospitalizations, prolonged length of hospital stay, and increased care cost for both patients and healthcare systems. Clinical decision support systems (CDSSs) have a great potential to support care providers to identify and manage DDIs timely, and thereby, to improve the quality of decisions on medication prescriptions. In this opinion paper, we provide a concise review of efforts necessary for a successful design and development of a DDI-CDSS with particular focus on the Iranian healthcare context. We also touch upon the measures to consider in order to overcome some of the important challenges and barriers jeopardizing the design and application of DDI-CDSSs in Iran.

Information Technology creates a foundation for developing health tourism. The purpose of the current study was to provide an information technology package for electronic infrastructures of health tourism.

In this review study, in order to find various dimensions of health tourism, articles related to electronic health tourism were searched in PubMed, Science Direct and Scopus databases, as well as Google Scholar and specific websites of health tourism. Searched key words included Medical Tourism, Health Tourism "AND" Information Technology, Information and Communication Technology, e-Tourism and Electronic Tourism. Criteria for selecting articles were access to their full texts as well as covering some aspects of information technology in the field of health tourism. The search yielded 117 articles, of which 32 articles were selected finally. The contents of these articles were carefully studied and the technology package of health tourism was presented.

Based on the findings, an information technology package was developed that included different types of technology tools, capabilities, and outcomes of information technology applications in health tourism. In this package, Internet was the main tool for developing health tourism, websites of health centers were the most important capability of these technologies, and creating a competitive and interactive health system was the main consequence of information technology application in the field of health tourism.

The designed package illustrates the dependence of positive outcomes of health tourism development to information-communication technologies. These tools provide the potential for gaining a share of the health tourism market and reform the health system structure to deliver high quality services in a global level.

In late 2019, Covid-19 Outbreak started in Wuhan, China and quickly spread through whole world. On the other hand, health information technology plays an important role in the current health system. Therefore, the aim of this systematic review investigates the role of electronic health during the outbreak of Covid-19 Crisis.

In this review study, studies from December 2019 to March 7, 2020 were searched for keywords and their equivalent in the "Medical Subject Headings (MeSH)" in the PubMed, Web of Science and Scopus databases also google search was used. From a total of 47 studies, 21 articles were identified relevant based on our research purpose. After applying the inclusion and exclusion criteria, finally five main articles and three reports were selected for review.

Studies evaluated the effective role of information technology in the diagnosis of Covid-19, prevalence, preventive, education and identification of high-risk areas. Among the technologies used were telemedicine, machine learning algorithms, deep learning, neural networks, and geographic information systems.

The results of this study showed that governments and medical informatics professionals can learn from the experiences of the role of technology in preventing Covid-19 outbreak for any future epidemic to prevent any threat to the lives of nations, the recession, and ultimately the immediate response in times of crisis.