Journal of Health and Biomedical Informatics
Year:2017 | Volume:4 | Issue:2 |Papers Count:8

Introduction: Nowadays, due to the increasing development of nursing science and demand for improving and enhancing the quality of nursing care, the necessity of integrating informatics with Nursing Science for proper management of nursing information flaunts more than ever. Therefore, the present study aimed to develop the electronic information system of nursing as a step towards the integration of informatics science with nursing science. Methods: The present study was conducted using technical action research approach. The main stages of the study were the development of a nursing electronic information system, its implementation, application and evaluation in a neonatal intensive care unit in 2016. Descriptive statistics was used for the analysis of quantitative data and qualitative data obtained from interviews were analysed using content analysis and coding. Results: The results of Satisfaction Scale Questionnaire showed satisfaction of nurses with the components and applications of the electronic system. Nurses' information about drugs and the process of nursing increased after using the system. Content analysis of qualitative findings also led to exploit the main categories of enhancing the quality of care, improving the documentation process, professional development, continuing education and learning, the integration of theory and practice in the application of the nursing process, human and equipment barriers. Conclusion: According to the obtained results, paying attention to the design of a native system based on the needs of nurses and the participation of the beneficiaries could take a small but successful step toward effective use of informatics science in nursing.

Introduction: Type 2 diabetes (T2D) imposes high expenses on societies, due to its effects on individual and social functions of patients. Adverse complications of this disease can be prevented through controlling patient's blood sugar level. The purpose of this study was to optimize the medication treatment of type 2 diabetes.Methods: In this applied survey, Markov decision process (MDP) was used to model the scheduling and sequencing of T2D medication treatments. In order to have a model close to reality, expense issues, time value of money (TVM) and inflation were considered and it was tried to plan a costeffective treatment program. Since anti-diabetic drugs cause hypoglycemia in elderly patients, a hypoglycemia preventive approach was applied on the proposed model and its effect was analyzed. The function of proposed model and efficiency of the obtained optimal policy were compared with clinical guidelines. Moreover, in order to address the model behavior, sensitivity analysis on parameters related to treatment costs was also conducted.Results: The proposed model showed better performance in glycemic control compared to clinical guidelines. Treatment cost parameters affected treatment program.Conclusion: In general, optimizing the medication treatment of T2D not only can improve patients' life period with quality, but also can decline high costs imposed on societies’ healthcare systems.

Introduction: Today, the use of information technology in education, is one of the most important goals of many educational institutions around the world and Iran, due to its advantages including the possibility to be used at any time and place. ACECR branch of Tehran University of Medical Sciences, as one of the organizers of specialized training courses, has already organized some e-learning courses. In this research, we tried to examine the role of each of the effective factors in e-learning in the success rate of this type of education. Methods: In this descriptive study, the effect of six factors, from the perspective of the four groups of participants, professors and ACECR (Iranian academic center for education, culture and research) staff and managers, was measured in 2010-2011, through using a researcher-made questionnaire including 39 questions on Likert scale. The questionnaire was sent via e-mail to 420 people and a total of 188 people participated in this study. The collected data were analyzed through SPSS software and using descriptive statistics and inferential statistics.Results: The results indicated the importance of all studied factors with the order of management, educational content, facilities, teachers, rules and learner.Conclusion: Although the difference between the effective factors is negligible, the extra effort of authorities to remove the shortcomings and weaknesses of the three main and effective factors of management, educational content and facilities needed for the success of e-earning is required.

Introduction: The prevalence of Crimean-Congo fever, a common disease between human and animal, shows an increasing rate by coming summer season. Detection of this disease by the use of necessary tests, lasts at least about one week. There are several data mining and machine learning techniques to create predictive models for identifying at risk people. In this study, C4.5 decision tree method has been used due to its simplicity and efficiency.Methods: In this applied descriptive study, data related to suspected cases of Crimean-Congo fever were used. These data have been collected from health centers of Iran in a four-year period since 2014 and contained 965 records with 29 features. First, by using the quadratic programming feature selection method, the variables which were effective on the model were selected and then, the C4.5 decision tree model was created through using input variables and determining the target variable. Data analysis was performed through Matlab software.Results: According to the applied model, it was found that fever, bleeding, sudden onset of symptoms, increased liver enzyms, increased total Bilirubin, decreased Hemoglobin, Hematuria, Leukocytosis, Proteinuria and Leukopenia have the greatest impact in the diagnosis of this disease.Conclusion: According to the obtained results, the sensitivity of the proposed model is 95% and its specificity is 50%. Therefore, this model showed acceptable efficiency in diagnosing this disease in comparison with other studies done in medical data mining field.

Introduction: With regard to the importance of Health Information Management in improvement of health care services, this study was done to determine the performance of Health Information Management Department of Valiasr hospital of Tehran city in 2017.Methods: This mix-method study was done in 2017. For data collection in quantitative study, a valid and reliable check list and in qualitative study, 8 interviews were used. Excel 2013 and MAXQDA were used for the analysis of quantitative and qualitative data respectively. Results: According to the results, medical coding ward had poorer performance than other wards (2.37 out of 3). The problems of Health Information Management Department of Valiassr hospital were categorized in five main categories including organizational, human resources, financial, infrastructural and hardware and knowledge and professional categories. Also, scan of main sheets was the most important performed activity.Conclusion: With regard to the results of the present study, it is suggested that policy makers pay special attention to Health Information Management.

Introduction: According to the importance of hypertension, the control and stabilization of blood pressure level is very important. The purpose of this study was to design a system for controlling and regulating arterial blood pressure by using sodium nitroprusside through a fuzzy controller. Methods: This is an applied study done through cross-sectional method. At first, related studies in the field of designing mathematical models for regulation of blood pressure were investigated. Then, by surveying the results of these studies, the best model was selected and necessary changes were made. In the final phase, the fuzzy controller was designed for blood pressure regulation. All processes of designing and implementation of Fuzzy system were performed in 2010 version of MatLab software.Results: In the first phase, the fuzzy controller caused rapid and sudden changes in output. Therefore, by modifying the controller to improve the system, the system was able to control the blood pressure after about 3 minutes and returned it to the normal level. But, again, the output of the controller had quick changes. So, by applying a filtered controller, high frequencies were controlled and blood pressure remained constant within about 1 minute. Conclusion: By using fuzzy control and valid mathematical models, blood pressure can be controlled and stabilized. Classical methods in dealing with biological systems are highly dependent on model parameters. But, fuzzy control, in facing uncertainty, provides an acceptable response with an acceptable speed.

Introduction: Pattern recognition field is necessary for the recognition of different sensorimotor tasks in Brain Computer Interface systems. Reducing the number of features is an important step in Brain Computer Interface systems and it can improve the accuracy and efficiency of the classification and reduce the costs.Methods: In this paper, features selection was performed through using Improved Binary Gravitational search algorithm and Advanced Binary Ant Colony Optimization on data related to brain signals of nine normal subjects for imagination of left and right hand movements. Features were extracted from six different frequency bands. Two classifiers including support vector machine and k- nearest neighbor were applied to separate the classes. Data were processed by EEGLAB toolbox and through matlab software.Results: The classification rate of the proposed method is 84.21%. Using feature selection methods, effective frequency bands and features for left and right hand movement classification were extracted.Conclusion: The results show the improvement in the classification rate by using Improved Binary Gravitational search algorithm and nearest neighbor classification.

Introduction: Electronic Health Record (EHR) contains valuable clinical information that can be useful for activities such as public health surveillance, quality improvement, and research. However, EHRs often contain identifiable health information that their presence limits the use of the records for sharing and secondary usages. De-identification is one of the common methods for protecting the confidentiality of patient information. This systematic review has focused on recently published studies on the usage of de-identification methods based on Machine Learning (ML) approaches for removing all identifiable information from electronic health records.Methods: A systematic review was performed in electronic databases like PubMed and ScienceDirect between 2006 and 2016. Studies were assessed for adherence to the CASP checklists and reviewed independently by two investigators. Finally, 12 articles were matched with inclusion criteria.Results: The selected studies have been discussed in terms of used methods and knowledge resources, types of identifiers detected, types of clinical documents, challenges and achieved results. The results showed that ML-based de-identification is a widely invoked approach to protect patient privacy when disclosing clinical data for secondary purposes, such as research. Also, the combination of the ML algorithms and some techniques such as pattern matching and regular expression matching could decrease need to train data.Conclusion: There is a lot of identifiable information in medical records. This study showed MLbased de-identification methods can intensively reduce the disclosure risk of information.