kobra etminani

The purpose of this study is to construct and validate a measurement model of women’s preferences in Obstetrician and Gynecologist (OB/GYN) selection in the private sector of non-clinical parameters. This methodological study included 462 respondents in OB/GYN’s offices to a researchermade questionnaire. The patients visited 57 offices of OB/GYNs in the city of Mashhad in Iran and completed women’s preferences in OB/GYN selection questionnaire over a 2-month period from January to February 2018. Exploratory Factor Analysis (EFA) was conducted to verify the instrument’s construct validity. Confirmatory Factor Analysis (CFA) was used to test whether the data fit our hypothesized model obtained from EFA model. The first draft of the questionnaire was prepared with 118 items based on literature review. The outcome of content validity assessment was a 51-item questionnaire. Scale-Content Validity Index (S-CVI) turned out to be 0.80. The results of EFA yielded an instrument with 33 items in six domains, which explained 52.657% of the total variance of the questionnaire. With performing CFA, the 6-factor model with 29 items demonstrated a good fit with the data (CFI=0.952, CMIN/DF=1.613, RMSEA=0.036). Availability and Accessibility, Communicational Skills, Office Environment, Recommendation by Others, Special Services, and Cost and Insurance were found to define the women’s preferences in OB/ GYN selection in private sector, Iran. The developed measurement model considers the patient’s preferences that influence decisionmaking process on OB/GYN selection. It can provide useful knowledge for OB/GYNs and policymakers to design appropriate and efficient marketing strategies according to the consumer preferences priority.

One of the most worrying aspects of medical area in developing countries is the Intensive Care Unit (ICU). This study aimed to evaluate the acceptability of the clinical dashboard by the users, prior to final use and their attitude towards this technology, as well as to examine the specific needs that Tele-ICU technology can cover in the form of a clinical dashboard.

This study was conducted at Shahid Bahonar Hospital of Kerman, Southeastern Iran, with three ICUs, the first, second, and third sections of which had 10, 12, and 24 beds, respectively. Taking survey and need assessment of care providers, qualitative and quantitative analyses were undertaken to identify key positive and negative themes. The data were analyzed by SPSS software version 18.

About 82% of care providers in the ICU participated in this survey. The number of participants based on the groups in the survey was 98 (81.7%) of the nurses and respiratory therapists group, 20 (80%) from the group of anesthesiologists and 20 (87%) from the group of anesthesiologist assistants who participated in the survey. About 51% of the survey participants completed the description section either partially or totally. On average, among all groups, the group of anesthesiologists had the most and the nurses had the least knowledge about telemedicine and Tele-ICU, whereas the anesthesiologist assistants had the most and the nurses and respiratory therapists group had the least knowledge about clinical dashboards.

This study showed that the level of knowledge and awareness of care providers, especially nurses and respiratory therapists in the ICU in terms of telemedicine and Tele-ICU is low and care providers are in doubt that telemedicine technology could have a positive or negative impact on human resource shortages, yet agreed that it would have a negative effect on the privacy of the patients and care providers. In addition, the ICU care providers agree that Tele-ICU can positively affect the quality of patient care, staff satisfaction, reduce the cost of care, and ease and reduce the time for patient counseling. This suggests the need for further research and education of system impact beyond patient outcomes related to this new technology.

Due to the thalassemia severe complications, prediction of mortality or patients survival has a great importance in early treatment phases. This study purpose was to predict the mortality rate of patients with thalassemia major and thalassemia intermedia, by the use of the binary logistic regression algorithm and genetic algorithm combination.

This retrospective cohort study was conducted on 909 thalassemia patients by using a questionnaire during 2004-2014. The data of all patients referring to Imam Reza Hospital from 2004 to 2014 have been considered. This study predictive variable is considered to be death or survival of the patient. In this research, we embedded the missing data by the use of the proposed data mining model and MICE algorithm. Totally, 100 patients were excluded from this research, due to the missing or out-of-range data. Death was considered as dependent variable. Also, a predictive model was designed in order to
predict the patient mortality using MATLAB language.

Mean age of the thalassemia patients was 25.7±9.04 years old and at the end of the study death was reported in 185 subjects. Additionally, there were also 26 independent variables. Moreover, the missing variables mean for each patient was 1.8±0.81. The combined predictive model was able to predict the patient survival rate with 94.35% accuracy. In this research, it was found out that 26 independent variables, which were collected from 12 variables were patient mortality predictors. Also, missing data imputation is an important method for increasing the data mining algorithms efficiency.

According to this study results, the use of missing algorithm with the data analysis aid yielded more accurate results, in comparison with the MICE algorithm. Furthermore, 12 parameters affected the patient mortality prediction, which were extracted by the genetic algorithm. Accuracy of the predictive model for the patient death detection was favorable. Consequently, it is recommended to use this model in order to predict the patient mortality.

Mobile health applications are growing and some Iranian mHealth apps have been installed more than 200,000 times, which indicates their popularity is increasing rapidly in Iran. The objective of the current study was to detect predicators of mHealth apps download to forecast the app market. This study reviewed all medicine and health apps available in the most popular Iranian Android app store “Cafebazaar” and collected apps information up to November 20, 2016. Of the 3331 studied apps, 24.4% were paid apps with prices ranging from 10,000 Rials to 300,000 Rials. The average number of apps published by a developer was eight. The effect of reviewed app variables was evaluated on the number of app downloads. There was heavy competition between mHealth app developers. There were several factors affecting the number of mHealth app downloads. mHealth apps with higher number of registered rating (P value < 0.001), Internet availability (P value < 0.001), and using the camera (P value = 0.03) were more likely to download. On the other hand, application price (P value < 0.001) and number of previous apps published by a developer (P value < 0.001) were inversely related to the number of app downloads. Considering these predicators would help developers to become more successful in the mHealth market.

The medicine prescription is the main nucleus of communication among the physician, pharmacist and patient. Generally, a good prescription quality reflects good prescribing process and thus good quality healthcare. Prescribing without correct dose, frequency, route of administration, timing or duration of treatment leads to repetitive of therapeutic and is effective in poor quality prescription. The purpose of this study is to identify the main items that must be written in prescription and being observed by physicians, which are effective in the quality of the Doctors' prescription. This research is an applied study in which the items affecting the quality of doctors' prescriptions were identified by Delphi technique in three phases. The statistical populations of this study are twelve experts, managers, officials and experts of the Medical Council of the Mashhad Pharmaceutical Research Committee. Based on the results of the Delphi technique, out of a total number of 35 items, 22 main items that should be written and observed in prescription by physicians, which are effective on the quality of physician prescriptions, were identified. Considering that one of the main forehead challenges of prescribing and rational use of drugs, is increasing the awareness of physicians about the principles of proper and scientific prescription. It is hoped that paying attention to these principles will reduce the medication errors in writing and interpreting the prescriptions as well.

As a prevalent metabolic disease, diabetes has different side effects and causes a wide range of co morbidity with a high rate of mortality. There is a need for certain interventions to manage this disease. Iranians usually have three main meals a day. Considering the special needs of diabetic patients and the possibility of hypoglycemia between the main meals, it is essential for these patients to eat something as a snack. Considering these conditions and the society’s orientation towards modern technologies such as smart phones, designing mobile-based nutrition recommender systems can be helpful. The snack recommender system is a knowledge-based smart phone application. This study has focused on the development of a recommender system that combines artificial intelligence techniques and makes up a knowledge base according to the guidelines posed by the American Diabetes Association (ADA). The snack menu was recommended in accordance with the patient’s favorites and conditions. The accuracy of the recommended menu was assessed in 2 steps. First, it was compared with the diet prescribed by three nutrition specialists. In the second step, system’s suggested menu was evaluated by the data from 30 diabetic patients using a valid questionnaire. The results of evaluating the snack recommender system by nutritionists showed that this system is capable of recommending various snacks according to the season (accuracy of 100%) and personal interests (accuracy of 90%) to diabetic patients. According to health nutritionists, the snacks suggested by this system are matched with Iranian culture. Moreover, the results revealed that a higher body mass index (BMI) makes the recommender system less sensitive to personal interests to suggest what is basically beneficial for one’s health. This study was a pioneering research to develop a more comprehensive dietary recommender system for diabetic patients which includes main meals as well. Patients found the system useful and were satisfied with the application. This system is believed to be able to help diabetic patients to take more healthy diet which leads to a better lifestyle

The study and analysis of each health system has become a necessity for its performance improvement through time. In this context, management and analysis of the number of patients is an important factor in the process of improving manager's decisions. The aims of this study were to explore and evaluate the use of multiple time series forecasting methods to predict monthly hospital inpatient admissions at six public hospitals in Mashhad city and to compare the accuracy performance of these methods.
This cross-sectional modeling study was performed based on monthly data of inpatient admissions at six public hospitals in Mashhad from March 2004 through March 2016. Data were extracted from database of the Statistics Office of Mashhad University of Medical Sciences. Holt-winters, Seasonal Autoregressive Integrated Moving Average (SARIMA), Multilayer Perceptron (MLP) and Generalized Regression Neural Networks (GRNN) models were applied to forecast monthly inpatient numbers at each hospital. The error of the models in regard to the predicted values was reported through Mean Absolute Percentage Error (MAPE).
Holt-Winters method, due to providing the optimal forecasting performance in four hospitals, could be an efficient method for predicting the number of inpatients in hospitals. Totally, the studied models with a MAPE from 2.13% to 4.12% showed acceptable performance in all six hospitals.
Time series analysis is an adequate practical tool for predicting the number of hospital inpatient admissions. Given the unique characteristics of different hospitals, applied methods in this study, including modeling and data analysis can be used in other hospitals to improve their resource allocation and strategic planning.

World Health Organization (WHO) estimates that the number of people with diabetes will grow 114% by 2030. It declares that patients themselves have more responsibility for controlling and the treatment of diabetes by being provided with updated knowledge about the disease and different aspects of available treatments, and diet therapy in particular. In this regard, diet recommendation systems would be helpful. They are techniques and tools which suggest the best diets according to patient’s health situation and preferences. Accordingly, this narrative review studied food recommendation systems and their features by focusing on nutrition and diabetic issues. Literature searches in Google scholar and Pubmed were conducted in February 2015. Records were limited to papers in English language; however, no limitations were applied for the published date. We recognized three common methods for food recommender system: collaborative filtering recommender system (CFRS), knowledge based recommender system (KBRS) and context-aware recommender system (CARS). Also wellness recommender systems are a subfield of food recommender systems, which help users to find and adapt suitable personalized wellness treatments based on their individual needs. Food recommender systems often used artificial intelligence and semantic web techniques. Some used the combination of both techniques.

Non-melanoma skin cancer (NMSC) has recently been one of the three most common cancers in Iran. Inappropriate management of the disease has led to an increase in the prevalence and overhead costs. Data mining techniques are helpful in the analysis of patient records and accurate management of diseases. This study aimed to find hidden patterns and relationships in the data of NMSC patients using data mining algorithms.
In this applied study, study population consisted of medical records of 828 NMSC patients referred to the Cancer Institute of Imam Khomeini Hospital in Tehran during 2006-2015. Demographic variables and NMSC risk factors were clustered using K-Means algorithm. Apriori algorithm was applied as well for extraction of association rules and determination of patient’s common information with a confidence of ≥ 0.9.
According to the studied variables, NMSC patients were classified in four clusters and three important factors influencing the disease were identified as abnormal BMI, high risk occupations and previous history of cancer. Seven rules were approved by association rules and the highest associations were found between the past history of the disease, the involved site, the relapse, and the type of NMSC.
For the first time, this study could highlight the most important factors affecting NMSC using data mining methods. These factors should be considered either in self examination or screening skin tests in high-risk groups. In future studies, the contribution of physiological, ecological and genetic factors in the development of skin cancer should be jointly investigated as well.

Non-Melanoma Skin Cancer (NMSC), including squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) is the most common cancer in Iran. This study aimed to infer implicit information in the cases of NMSC patients referred to the Cancer Institute of Tehran.
A number of 865 cases of NMSC related to 2007-2015 years were selected from the archives of Cancer Institute using simple random sampling method. Initially, epidemiological variables, were extracted and after preprocessing of the data, descriptive statistics were used to classify and graphic display of information.
The mean age for patients was identified 65 years and the highest and lowest numbers of cases were in age groups of 69-60 and 19-10 years respectively. The incidence of SCC was higher than BCC in the first four age decades and this trend was reversed in the next decades. About 56% of cases were BCC and 44% were SCC and frequency of NMSC in men was 2/33 times more than women. The head/neck and trunk were the highest and lowest involved positions. The disease had relapsed at least twice in 32% of cases. The disease history showed that most of patients were previously affected by kinds of cancers, cardiovascular or metabolic diseases. The occupation of 114 patients was found to be as high-risk jobs. Most patients were referred to Cancer Institute from northern and western parts of the country.
Along with other studies, we found more cases of BCC than SCC, involvement of patients over 60 years at the head-neck and a previous history of cancer. But against other studies, SCC cases was found more in the first four decades of life than BCC cases, feet affected twice than hands and the locality of most cases was found in high latitudes. Patient records are potential resources that can help management of the diseases.

Adult T-cell leukemia/lymphoma (ATLL) is caused by human T-cell lymphotropic virus type-1 (HTLV-1). HTLV-1 oncogenes can induce malignancy through controlled gene expression of cell cycle checkpoints in the host cell. HTLV-I genes play a pivotal role in overriding cell cycle checkpoints and deregulate cellular division. In this study, we aimed to determine and compare the HTLV-1 proviral load and the gene expression levels of cyclin-dependent kinase-2 (CDK2), CDK4, p53, and retinoblastoma (Rb) in ATLL and carriers groups. A total of twenty-five ATLL patients (12 females and 13 males) and 21 asymptomatic carriers (10 females and 11 males) were included in this study. TaqMan real-time polymerase chain reaction assay was used for evaluation of proviral load and gene expression levels of CDK2, CDK4, p53, and Rb. Statistical analysis was used to compare proviral load and gene expression levels between two groups, using SPSS version 18. The mean scores of the HTLV-1 proviral load in the ATLL patients and healthy carriers were 13067.20±6400.41 and 345.79±78.80 copies/104 cells, respectively (P=0.000). There was a significant correlation between the gene expression levels of CDK2 and CDK4 (P=0.01) in the ATLL group. Our findings demonstrated a significant difference between the ATLL patients and healthy carriers regarding the rate of proviral load and the gene expression levels of p53 and CDK4; accordingly, proviral load and expression levels of these genes may be useful in the assessment of disease progression and prediction of HTLV-1 infection outcomes.

The outbreak of gestational diabetes has a significant increase during recent years. This disease has complications for mother and her baby. Screening is an opportunity for preventing of gestational diabetes complications.

The aim of this research was to determine the most important risk factors for Gestational Diabetes Mellitus (GDM) in Iran according to the expert's views by Group Analytical Hierarchy Process.

In this cross-sectional study, papers related to the prevalence and risk factors of GDM in Iran from 1992-2015 were reviewed. By studying texts and Up to Date databases, 10 risk factors for gestational diabetes were collected. Among these 10 items, the risk factors that have become significant based on studying literature in Iran were selected for analysis. Group Analytical Hierarchy Process (GAHP) questionnaire distributed among all experts.

8 risk factors of gestational diabetes were significant in Iran. The analysis of expert's views showed that "History of GDM or disorder in glucose tolerance in pregnancy" is the most important risk factor for developing GDM (40.7%). The second and third most important risk factors were "History of macrosomia (infant birth weight > 4.1 Kg)" (20.2%) and" History of diabetes in first degree relatives" (10.7%).

Suggesting screening based on the determined order of these risk factors can reduce the cost and stress in pregnant women. Also, it makes patient identifying faster. The healthcare sector can consider these priorities determined in expert's views to prevent gestational diabetes.

Pathology reports generally use an unstructured text format and contain a complex web of ýrelations between medical concepts. In order to enable computers to understand and analyze ýthe reports’ free text, we aimed to convert these concepts and their relations into a structured ýformat.ý
The training, validation, and evaluation of this implementation study was based on a corpus ýof 258 pathology reports with a positive diagnosis of celiac disease randomly selected from ýamong the records of 2 pathology laboratories. Our proposed system consisted of 3 phases of ýstandardization of celiac disease pathology reports using Delphi technique with 3 experts, ýinformation extraction from free text reports with text mining techniques using Stanford ýParser, and automatic classification of celiac disease stages in marsh system using decision ýtree classifier J48 algorithm.ý
We were successful in extracting information from free text pathology reports and assigning ýeach piece of information to the associated pre-defined fields in standardized template form ýwith an accuracy of 76%. After determining marsh stage for each report in the third phase, ýour system showed an average overall accuracy of 62%. Evaluation of the third phase as an ýindependent system with manually corrected, gold-standard input achieved an accuracy of ýgreater than 84%.ý
The benefits of standardized synoptic pathology reporting include enhanced completeness ýand improved consistency, avoidance of confusion and error, and facilitation of the faster and ýsafer transmission of critical pathological data in comparison with narrative reports.ý

Brucellosis is a transmissible disease between humans and animals through infected animals and their products.The disease exist in most parts of the world especially in developing countries.because of the serious impact of the disease in public health and socio-economical status, controling the disease is very important in developing countries. The purpose of this article is to identify hidden patterns and relations between brucellosis patients which can be benefitial for physicians in diagnosis process.
Material and This study is a retrospective study of data collected from brucellosis Khorasan Razavi province recorded at the health center, have been used. Due to differences in format and number of features collected during different years, before processing operations carried out in several stages to the same data. Fields associated with different methods and with expert opinion was converted into discrete fields and fields lost was estimated using the EM algorithm. APPIORI algorithm analysis was performed using the hidden relationships between data found that significant relationships were infected with expert opinion.
Among the 163 relationship with over 7.0 Conficence rate which Weka software was discovered, by the application in consultation with an infectious disease expert, 10 clinically significant relationship was reported.
Diagnosig brucellosis is realy difficult to physicions because of its vagious nature and symptoms. Because many unknown relationships between risk factors and demographic characteristics of the patients, the use of data mining concepts, especially in the medical data is beneficial because usually high volume assumptions are available. further studies can test the validity of these rules like Randomize Control Trial studies.

Background & Brucellosis is a transmissible disease between humans and animals that is transmitted through infected animals and their products. Razavi Khorasan which is classified as a high incidence province, considered as one of the notable areas for controlling the disease. The aim of this study is to identify epidemiological characteristics of the disease in Razavi Khorasan.
Data on 5743 cases in Razavi Khorasan (affiliated to Mashhad Medical University) during 5 years were analyzed.
The incidence of the disease in the province is 26 out of 100,000 people which the incidence rate in Daregaz,Takhtjolge and Roshtkhar was much higher. In the first 3 years of study the disease incidence was ascending and in last 2 years it was descending and from May to August the prevalence was more than other months.85% of patients were rural and 15% were urban. 56.9% were male and 43.1% female. Household and farmer-rancher were the most common jobs. Average age of patients was 33.4 ±18.1 year. 77.2% had consumption of unpasteurized dairy products which milk (91.4%) and cheese (21.4%) had the highest consumption. The most common symptoms were fever (79%) and anorexia (47.5%). A significant relationship between location and both livestock vaccination history and also family history, was found.
Because of higher incidence rate of the disease in Daregaz, Takhtjolge and Roshtkhar, and also young people aged 10 to 30 years and villagers, It’s need to control the disease in these groups.

Gestational diabetes could have harmful consequences on Children’s health. Since the initiation of gestational diabetes is simultaneous with brain evolution, this study is designed to predict evolutionary growth in children of mothers with gestational diabetes.
In this study, the required data were obtained through investigating the profiles of pregnant women referring to Mashhad’s health centers during 2011 to 2013. The data collection tool was child’s periodical examination form. The data were predicted using memory based learning algorithms, decision tree, and demographic features of mother
The accuracy of prediction of children's cognitive ability status for three groups of healthy mothers (92), mothers suffering from gestational diabetes (83), and both groups (175) were separately evaluated through decision tree learning algorithms based on memory with three precision, ­recall and F-measure criteria. Memory based learning algorithm showed better results than decision tree in all cases.
Since growth delay in the cognitive-intellectual ability of child imposes high costs to health care system in the future, preventive measures should be adopted by prediction of the level of child's cognitive skill in prenatal stage to avoid probable problems.

Hospital beds and resources are extremely limited for patients. A few factors such as Length of Stay or LOS affected Availability of Hospital Resources. Identifying factors associated with this benchmark index can be valuable in planning to optimize the utilization of the existing resources. Aiming to identify factors associated with length of stay based on admission data, the present study has been conducted. This survey is a descriptive-retrospective study. We extracted 449,678 patient data from computerize patient records in Emam Reza and Ghaem Hospital in Mashhad, Iran. After data cleaning and preprocessing, univariate analysis was conducted using t-test and one-way ANOVA test. Multiple linear regression was also used to determine the factors associated with LOS. All analyses were conducted with SPSS statistical software version 19. The mean LOS in Emam-Reza and Ghaem hospital were 5.5 and 6.5 days. Age, ward, admission reason, referral status, admission month and day on month, insurance type, residence type, marital status and patient job were associated with length of stay. No significant differences in LOS were found by payment type, admission day on week, gender, doctor and patient Companionship. LOS can be predicted by socio-demographic and clinical factors using data mining models on hospital admission data. The procedure can be a useful tool for planning and optimal resource allocation in hospitals.

The past few years have shown an increase in the amount of data that are being generated in all fields. This increase is due to human needs to all aspects from business to science. One of these fields is medicine with uncertain data. For these amount of data there exists computational methods and tools for analysis and it requires medical informatics researchers and specialists to choose the most appropriate method to cope with these data. This paper presents one of the common data mining tasks, association rule mining, when considering particular continuous time series measurements. The most important goal of this study is dealing with and processing time series data for the purpose of extracting linguistic rules. Also, comparing the results of this method with the results of classical statistical analysis is the second objective. A three-part analytical pipeline is used in this study as a new mining approach, consisting of data preparation, data transformation, and data analysis to elicit knowledge about the interaction of the parameters measured and the effect of different drugs on the rats. Weka data mining toolkit has been employed for extracting knowledge in the form of association rules and the important rules identified by this toolkit are interpreted for their medical significance. Discretizing time series data and linguistic representation of rules have been demonstrated. This study exhibits this new approach by analysis of the data of the cardiovascular responses to specific medications in rats that have been recorded by lab chart software in the laboratory. Once the time series was discretized, simple association rule mining methods are used to extract rules. The association rules that are discovered can provide the domain expert with new knowledge about the possible effects of injected rats or to help to improve annotation consistency.

The Length of Stay (LOS) in the hospital is used as an indirect indicator of resources consumption and efficiency in hospitals. Identifying factors associated with this systematic review can be valuable in planning to optimize the utilization of the existing resources. The goal of the present study was to investigate factors associated with length of stay and it has been conducted as a systematic review. In this systematic review, papers were retrieved by the use of specified key terms in their titles and no restricted time in Persian and English databases. Papers were selected according to how they were in line with the criteria for inclusion and exclusion and finally, information were extracted and entered to Excel 2010 software for analysis. 18 articles out of 347 were selected. These studies introduced four criteria associated with length of stay including clinical, demographic, administrative, and hospital factors. Applied methods for identifying these criteria were statistical techniques and data mining techniques such as decision tree regression and artificial neural networks. The goal of all studies was making a new model for identifying factors associated with LOS or was evaluating other methods introduced in other studies. Findings of this study represent that identifying factors associated with LOS can be variable according to data collection place, studied variables, and applied data mining techniques. So we suggest researchers to help hospital managers and planners with identifying and reducing factors associated with LOS.