Journal of Health and Biomedical Informatics
Year:2018 | Volume:4 | Issue:4 |Papers Count:8

Introduction: Electronic health record is electronic data of “entire life of a person” registered by healthcare providers and shared in different health centers. The electronic health record has been announced to all medical universities of the country since 2015 by launching the Integrated Health System (SIB). This study aimed to determine the satisfaction of urban family physicians and health care providers in Fars and Mazandaran provinces from SIB.Methods: This cross-sectional study was conducted in the winter of 2016 on urban family physicians and health care providers who were selected by systematic random sampling. Data collection tool was a researcher-made questionnaire consisting of 2 parts: demographic variables with 8 open and closed questions and satisfaction with 8 closed questions on Likert scale. The validity and reliability of the questionnaire were confirmed.Results: The total number of 464 participants included 236 (50.9%) physicians and 228 (49.1%) health care providers. From these, 273 (58.8%) were employed in Fars province and 191 (41.2%) in Mazandaran province. The majority of participants (65.5%) were women. mean score of satisfaction of SIB (from 5 points) in general was 2.94±0.8, and it was 2.89±0.8 in the Fars and 3.03±0.7 in the Mazandaran provinces. Mean score of satisfaction was 2.75±0.7 in urban family physicians and 3.04±0.8 in health care providers. Satisfaction level showed significant difference based on participants' type of occupation (P=0.0001) and province (P=0.122).Conclusion: The results showed that the satisfaction of employees was lower than the average level. It is recommended to redesign the structure of SIB more consistent with the needs of employees and with a user-oriented approach.

Introduction: Hospitals as the most important consumer of resources in healthcare section are highly sensitive to maximum consumption of minimum existing resources. In the recent two decades, computer data processing to extract knowledge and improve utilization of resources has attracted the attention of organizations. In this study, allocation of CCU beds in Shahid Faghihi Hospital of Shiraz is investigated through optimizing and combining Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA).Methods: In this analytic cross-sectional study, patients were monitored through optimization and combination of genetic algorithm and imperialist competitive and allocated to CCU beds in the spring of 2016. To this end, number of patients, number of beds, number of doctors and vital signs of patients were used as input and configuration of patients and allocation optimization were considered as output. MATLAB 2012 was used to analyze data.Results: Results of this study show that ICA is more efficient compared to GA in optimization of allocating CCU beds to patients. Moreover, the hybrid algorithm obtained from combination of ICA and GA is more efficient than ICA.Conclusion: In the process of this study, priorities of patients' hospitalization and also manner of hospitalization were determined and suggestions for allocation of beds to patients were presented.

Introduction: Breast cancer is the most common form of cancer in women. Breast cancer detection is considered as one of the most important issues in medical science. Diagnosis of benign or malignant type of cancer reduces costs and also is important in deciding about the treatment strategy. The aim of this study was to provide data mining based models that have the predictability of breast cancer detection.Methods: This study was descriptive-analytic. Its database included 683 independent records containing nine clinical variables in the UCI machine learning. Multilayer Perceptron artificial neural network, Bayesian Neural Network and LVQ neural network were used for classification of breast cancer to benign and malignant types. In this study, 80% of data were used for network training and 20% were used for testing.Results: After pre-processing the data, different neural networks with different architectures were used to detect breast cancer. In the best condition, we could predict benign or malignant cancer in the MLP neural networks, LVQ and Bayesian Neural Networks with an average of ten tests with an accuracy of 97.5% and 97.6% and 98.3% respectively. Our investigations showed that Bayesian neural network had a better performance.Conclusion: Breast cancer is one of the most common cancers among women. Early diagnosis of disease reduces healthcare costs and increases patient survival chance. In this study, using data mining techniques in diagnosis, the researchers were able to use Bayesian neural network to achieve high accuracy in diagnosis.

Introduction: The continuous increase in the demand for specialized skin care services, together with the unbalanced geographical distribution of these specialists, has limited the access of patients to these services. Teledermatology is one of the innovative approaches that can be considered as a solution to improve the access to specialized skin care services with minimal material resources in developing countries such as Iran.Methods: The present study is an applied and aimed to re-designing and evaluating tele-dermatology software. In this research, redesign of the software was done through obtaining the users feedback after being used in clinical setting, then final version of the software designed and usability evaluation test was conducted.Results: The usability testing result, show dermatologist and GP were very satisfied with the system and overall average scores obtained were 8 out of a total of 9 achievable scores. The scores earned was respectively: 7.8 for overall reaction to the system, 8.5 for screen feature, 7.9 for terminology and system information, 8.3 for Learning and 7.5 for system capability.Conclusion: The experience gained from this research shows that for the successful development and use of telehealth tools, need engage and collaboration with main stakeholder, identify, understand and modeled work flows and processes related to the provision of patient services and treatment. Also in order to increase the usability and user satisfaction, the design and development of systems should be based on the principle of user-centered design and engagement of system stakeholders at all stages of system design and development.

Introduction: Metabolic syndrome is a group of risk factors for developing cardiovascular diseases and diabetes in an individual. The presence of various signs and symptoms makes the diagnosis of this disease difficult. Data mining can provide clinical data analysis of patients for medical decision-makings. The purpose of this study was to provide a model for increasing the predictive accuracy of metabolic syndrome.Method: In this applied-descriptive study, the medical records of 1499 patients with metabolic syndrome with 15 characteristics were investigated. Patients' information is collected from the standard database of Yazd Shohada-ye kargar Hospital. Each patient was followed for at least one year. In this paper, GBC algorithm was used to optimize the results of KNN data mining algorithm to predict and diagnose metabolic syndrome, and a new model was presented.Results: Based on the objective function to predict the increase of blood lipids in the proposed method, gray wolf algorithms, particle swarm and genetics were used to improve the performance of the KNN algorithm. The analyses show that the proposed model with the precision accuracy of 0.921 has a greater accuracy compared to fuzzy methods, backup vector machine, tree decomposition and neural network.Conclusion: Search in medical databases for the purpose of obtaining knowledge and information to predict, diagnose, and decision making are some applications of data mining in medicine. Hereditary algorithms can be used to optimize data mining techniques. The prediction and proper diagnosis of metabolic syndrome by using artificial intelligence and machine learning increases the chance of successful treatment.

Introduction: Nocardia is one of the most important aerobic actinomycetes that lives in soil and can enter the human body by inhalation and traumatic inoculation and causes Nocardiosis. Molecular methods are one of the best methods for rapid and accurate identification and differentiation of species of this group of bacteria. The purpose of this study was evaluation of three housekeeping genes in identification and differentiation of most important species of Nocardia.Methods: In this study cross-sectional, first, gene sequences of 16S rRNA, gyrB and hsp65 for ten species of Nocardia were obtained from Genebank (NCBI). Then, those sequences were transferred to MEGA 5.0. Finally, phylogenetic trees based on each of 16S rRNA, gyrB and hsp65 genes were drawn.Results: Phylogenetic trees analysis based on 16S rRNA, gyrB and hsp65 gene sequences indicated that all of these genes could identify and differentiate Nocardia species. Also, it was found that gyrB gene is the best option for drawing the phylogenetic relationships of Nocardia species.Conclusion: According to this research, for accurate identification of Nocardia species, several housekeeping genes should be investigated simultaneously.

Introduction: Mobile technology has provided new opportunities for health care systems.Improvement of health services outcomes in different patient groups is one of the benefits of using this tool. Although the use of mobile in Iran is expanding, there is no evidence of the state and the use of this technology in health system. The aim of this study was to review published researches on the application of mHealth in the health system of Iran.Methods: In order to carry out a review study, Pubmed database was searched by the keyword "mobile Health" and its equivalents which have derived from the "Medical Subject Headings".Iranian databases including Iran medex, Magiran and Scientific Information Database (SID) were also searched for Persian and English terms of mobile health. Retrieval citations from information databases were sent to the endnote software and evaluated based on the considered criteria.Results: The research sample consisted of 26 articles that met the criteria of the study. In most of studies, text messaging was the main intervention tool of mHealth. The results indicated significant effect of mobile health in improving the patients' care.Conclusion: In Iran, mobile health can be effectively used in the health system due to population structure and geographic extent. According to the results of this study, the use of mobile health, especially in educating patients for self-care and preventing the spread of diseases, can be very effective.

Introduction: Ontologies improve the quality of information retrieval (IR). Therefore, it is important to better know the process of ontology engineering and their application in IR. The aim of this research was to identify the engineering methods of ontologies and also their application in biomedical IR.Method: This review was done through library research method with an analytical approach.Required information for the review was retrieved from Pubmed, Scopus and Web of Science using keywords: "Ontology-based biomedical Information Retrieval", "Information Retrieval", "Biomedical Information Retrieval", "Ontology engineering", "Ontology construction", "Biomedical Ontology" and "Ontology building" without time limit. Five articles addressing an ontology-based biomedical IR system were reviewed.Results: Studies on ontology based biomedical IR, started in 2004, are not limited to a single country. In general, ontologies are used to manage the semantic metadata. Although most IRs try to develop their own ontologies, reuse of earlier ontologies is a priority. The material for developing ontologies is taken from the literature in the domain. The studied ontologies have been produced by centralized approaches and decentralized approaches and different groups have not been used.Conclusion: The main purpose of systems for applying ontologies is using them to develop semantic metadata for helping machine reasoning.